DOT HS 811 681 November 2012

Evaluation of the Effectiveness 

Of TPMS in Proper Tire Pressure 

Maintenance
DISCLAIMER

This publication is distributed by the U.S. Department of Transportation, National Highway 

Traffic Safety Administration, in the interest of information exchange. The opinions, findings, 

and conclusions expressed in this publication are those of the authors and not necessarily those 

of the Department of Transportation or the National Highway Traffic Safety Administration. 

The United States Government assumes no liability for its contents or use thereof. If trade names, 

manufacturers’ names, or specific products are mentioned, it is because they are considered essential 

to the object of the publication and should not be construed as an endorsement. The United States 

Government does not endorse products or manufacturers.

Suggested APA Format Citation:

Sivinski, R. (2012, November). Evaluation of the effectiveness of TPMS in proper tire pressure 

maintenance. (Report No. DOT HS 811 681). Washington, DC: National Highway Traffic Safety 

Administration.
i 

Technical Report Documentation Page

1. Report No.

DOT HS 811 681

2. Government Accession No. 3. Recipient’s Catalog No.

4. Title and Subtitle

Evaluation of the Effectiveness of TPMS in Proper Tire

5. Report Date

November 2012

Pressure Maintenance 6. Performing Organization Code

7. Author

Robert Sivinski

8. Performing Organization Report No.

9. Performing Organization Name and Address

Evaluation Division; National Center for Statistics and Analysis

10. Work Unit No. (TRAIS)

National Highway Traffic Safety Administration 11. Contract or Grant No.

Washington, DC 20590

12. Sponsoring Agency Name and Address

National Highway Traffic Safety Administration

13. Type of

NHTSA

Report and Period Covered

Technical Report

1200 New Jersey Avenue SE. 14. Sponsoring Agency Code

Washington, DC 20590

15. Supplementary Notes

16. Abstract

This report is an analysis of the data collected through the Tire Pressure Monitoring System‐Special Study

as it pertains to the effectiveness of TPMS in promoting proper tire inflation.  The study was conducted in

2011, using a nationally representative sampling structure, based on the primary sampling units (PSUs) of

the National Automotive Sampling System.  NASS personnel collected 6,103 complete vehicle observations

including tire pressure of all four tires.  This survey found that 23.1 percent of the MY 2004‐2007 vehicles

without TPMS had at least one severely underinflated tire as defined by FMVSS No. 138 (25% or more

below the vehicle manufacturer’s recommended cold tire pressure), but only 11.8 percent of the MY 2004‐

2007 vehicles equipped with TPMS had a severely underinflated tire.  Based on these results, the presence

of TPMS on a vehicle of model years 2004 to 2007 is estimated to result in a 55.6‐percent reduction in the

likelihood that the vehicle will have one or more severely underinflated tires as defined by FMVSS No. 138.  

It is also estimated to result in a 30.7‐percent reduction in the likelihood that the vehicle will have one or

more tires that are overinflated by 25 percent or more above the manufacturer’s recommended cold tire

pressure.  During the first eight years of operation TPMS is estimated to save a typical passenger car 9.32

gallons of fuel and a typical LTV 27.89 gallons of fuel.  During 2011 TPMS is estimated to have saved $511

million across the vehicle fleet through reduced fuel consumption.  NHTSA plans to conduct further

research to determine the effect of TPMS on the incidence of tire‐related crashes and injuries.

17. Key Words

NHTSA; TPMS; Tire Pressure Monitoring

18. Distribution Statement

Document is available to the public from the National

System; inflation; FMVSS 138; Technical Information Service www.ntis.gov

effectiveness

19. Security Classif.

Unclassified

(Of this report) 20. Security Classif.

Unclassified

(Of this page) 21. No.

58

of Pages 22. Price

Form DOT F 1700.7 (8‐72)
ii

Table of Contents

Executive Summary......................................................................................... iv

1. Introduction

1.1: Risks of Over/Underinflation ..................................................................... 1

1.2: Tire Pressure Monitoring Systems............................................................. 1

1.3: Past Research............................................................................................. 4

1.4: Goals of the Evaluation.............................................................................. 4

2. Survey Methods

2.1: Survey Design............................................................................................. 5

2.2: Site Cooperation ........................................................................................ 6

2.3: Survey Participants .................................................................................... 7

2.4: Data Collection Staff .................................................................................. 7

2.5: Data Collection Schedule ........................................................................... 8

2.6: Data Collection Equipment........................................................................ 8

2.7: Forms and Variables .................................................................................. 9

2.8: Questionnaire Development and Training .............................................. 10

2.9: Data Entry and Quality Control................................................................ 11

2.10: Weighting and Estimation ..................................................................... 11

2.11: Data Corrections and Adjustments........................................................ 12

3. Underinflation

3.1: Summary .................................................................................................. 13

3.2: Population Estimates............................................................................... 13

3.3: TPMS Effectiveness.................................................................................. 15

3.4: Effectiveness by Display and System Type .............................................. 18

3.5: Effectiveness by Vehicle Type.................................................................. 20

4. Overinflation

4.1: Summary .................................................................................................. 22

4.2: Population Estimates............................................................................... 22

4.3: TPMS Effectiveness.................................................................................. 24

4.4: Effectiveness by Display and System Type .............................................. 26

4.5: Effectiveness by Vehicle Type.................................................................. 27
iii

5. Fuel Economy

5.1: Summary .................................................................................................. 28

5.2: Average Underinflation............................................................................ 28

5.3: Effect of TPMS on Fuel Economy............................................................. 30

6. Discussion

6.1: Summary of results.................................................................................. 36

6.2: Possible Sources of Bias........................................................................... 37

6.3: Limitations ............................................................................................... 38

6.4: Future Research....................................................................................... 39

7. Appendix

7.1: Weighting................................................................................................. A1

7.2: Survey Forms............................................................................................ A4
iv

Executive Summary

This report presents an analysis of the data collected through the Tire Pressure Monitoring SystemSpecial Study (TPMS-SS) as it pertains to the effectiveness of TPMS in promoting proper tire inflation. 

The TPMS-SS was conducted in 2011, using a nationally representative sampling structure based on the 

primary sampling units (PSUs) of the National Automotive Sampling System. NASS personnel collected

6,103 complete vehicle observations (4,391 of which were equipped with TPMS) including tire pressure 

and temperature of all four tires. Only vehicles in the model year range 2004-2011 were surveyed.

Proper tire inflation is important for several reasons. Underinflated tires experience a greater amount 

of sidewall flexion than properly inflated tires, resulting in decreased fuel economy, sluggish handling, 

longer stopping distances, increased stress to tire components, and heat buildup that can lead to 

catastrophic failure of the tire, such as cracking, component separation, or blowout. These catastrophic 

failures can cause loss of vehicle control and may result in a crash. Overinflated tires may be more easily 

damaged by potholes or debris. Severe overinflation may increase stopping distance due to reduced 

area of road contact and non-optimal traction, and may also contribute to vehicle instability. As with 

underinflation, overinflation may result in uneven tread wear that reduces the useful life of the tire. 

This report does not attempt to directly measure the relationship between TPMS and vehicle safety;

rather it measures the relationship between TPMS and proper tire inflation. In order to estimate the 

effect that TPMS has on crash avoidance and mitigation, future analyses are planned that will use realworld crash data.

This survey found that 12.4 percent of all passenger vehicles in the US of model years 2004-2011 have at 

least one tire that is severely underinflated as defined by FMVSS No. 138 (25% or more below the 

vehicle manufacturer’s recommended cold tire pressure). The survey also found that 23.1 percent of 

the MY 2004-2007 vehicles without TPMS had at least one severely underinflated tire, but only 11.8

percent of the MY 2004-2007 vehicles equipped with TPMS – and only 5.7 percent of the more recent, 

MY 2008-2011 vehicles equipped with TPMS – had a severely underinflated tire. Based on the data 

from model year 2004-2007 vehicles (the range of collected model years that contained both vehicles 

with and without TPMS), TPMS was estimated to result in a statistically significant 55.6-percent

reduction in the likelihood that a vehicle will have one or more severely underinflated tires. 

TPMS is also estimated to result in a 30.7-percent reduction in the likelihood of severe overinflation 

(25% or more above the manufacturer’s recommended cold tire pressure) for model year 2004-2007

vehicles. This effect was present in vehicles with TPMS systems that do not alert the driver to 

overinflation, and it is unclear what causes the association between TPMS and reduced overinflation. 

Although the specific effects of overinflation are not well documented, it is reasonable to assume that it 

results in more rapid tire wear and, possibly, reduced vehicle stability.

While the primary goal of TPMS is to reduce underinflation in order to make vehicles safer to operate, a 

further benefit of reduced underinflation is improved fuel economy. By combining estimates of reduced 

underinflation due to TPMS with estimates of increases in fuel economy resulting from increases in tire 

pressure, it’s possible to estimate the amount of fuel that TPMS will save an average vehicle during a 
given period of time.  During the first eight years of operation, TPMS is estimated to save a typical

passenger car 9.32 gallons of fuel and a typical LTV 27.89 gallons of fuel.  During 2011 TPMS is estimated

to have saved $511 million across the vehicle fleet through reduced fuel consumption.  This estimate

does not include any additional savings that may result from extended tire life or any crash‐avoidance

benefits.  

Future analyses of TPMS effectiveness will be conducted using crash data to directly measure the

relationship between TPMS and tire‐related crashes.  Also, future analyses are planned to analyze the

interview data collected through the TPMS‐SS.  This interview data includes information about driver

behavior as it pertains to tire care and maintenance.

v 
1

1: Introduction

1.1: Risks of Under/Overinflation

Proper tire inflation is important for several reasons. According to the Rubber Manufacturers’ 

Association, underinflation is the most common cause of tire failure.1

 Underinflated tires experience a 

greater amount of sidewall flexion than properly inflated tires, resulting in decreased fuel economy, 

sluggish handling, longer stopping distances, increased stress to tire components, and heat buildup that 

can lead to catastrophic failure of the tire, such as cracking, component separation, or blowout.

2

 These 

catastrophic failures can cause loss of vehicle control and may result in a crash. Overinflated tires may 

be more easily damaged by potholes or debris. Severe overinflation may increase stopping distance due 

to reduced area of road contact and non-optimal traction, and may also contribute to vehicle instability. 

As with underinflation, overinflation may result in uneven tread wear that reduces the useful life of the 

tire. 

This report evaluates the effectiveness of tire pressure monitoring systems (TPMS) in reducing the 

frequency of severely underinflated tires by using survey data acquired through the Tire Pressure 

Monitoring System – Special Study (TPMS-SS), specifically designed for this purpose. (Future analyses 

are planned to estimate the effect that TPMS has on crash avoidance and mitigation, based on realworld crash data.) Statistical survey sampling techniques were used to generate a nationally 

representative sample of passenger vehicles of model years 2004-2011. The TPMS-SS collected data on 

6,503 vehicles at gas stations throughout the United States from August 2010 through April 2011. Over 

90 percent of these observations (n = 6,103) were complete enough to include in analyses of TPMS 

effectiveness. The basic method of analysis was to compare under- and overinflation rates observed in 

vehicles with and without TPMS. The magnitude of the difference in these rates formed the estimates 

of TPMS effectiveness.

1.2: Tire Pressure Monitoring Systems

Section 13 of the Transportation Recall Enhancement, Accountability, and Documentation (TREAD) Act, 

which Congress passed on November 1, 2000, directed NHTSA to conduct rulemaking actions to revise 

and update the Federal Motor Vehicle Safety Standards (FMVSS) for tires, to improve labeling on tires, 

and to require a system in new motor vehicles that warns the operator when a tire is significantly 

underinflated. In response, in 2005 NHTSA published the final rule for FMVSS No. 138 which requires 

that passenger cars, multipurpose passenger vehicles (MPVs), and trucks and buses with a gross vehicle 

weight rating (GVWR) of 10,000 pounds or less be equipped with a TPMS that is capable of detecting 25 

percent underinflation in any combination of tires. The rule specified a phase-in schedule from 2005 

 1 Rubber Manufacturers Association. “Tire and Auto Safety.” [Online] Available at

http://www.rma.org/tiresafety/auto_safety_facts.html, March 2001. 

2 Final Regulatory Impact Analysis, Tire Pressure Monitoring System, FMVSS No. 138, March 2005, NHTSA, Docket 

No. 2005-20586-2.
2

through 2007, with all vehicles manufactured on or after September 1, 2007, required to be equipped 

with TPMS. 

These systems must alert the driver to low tire pressure (pressure 25% or more below the 

manufacturer’s recommended cold tire inflation pressure, which is typically located on the door jamb 

label) through a dash displayed warning light. The display must activate within 20 minutes of

underinflated travel at speeds of 50-100 km/hr and must remain illuminated until the underinflation is 

remedied. The system must also have a malfunction lamp in addition to a low pressure warning lamp 

that alerts the driver if the vehicle’s TPMS is not functioning properly. Additional details of the 

requirements of FMVSS No. 138 can be found in the Code of Federal Regulations.

3

Two distinct TPMS technologies have been installed in production vehicles at various times since 2000:

Direct TPMS: Also known as pressure sensor based (PSB), direct systems use a physical pressure sensor 

inside each tire that sends information to a central processing unit in the vehicle. Some direct systems

exceed the minimum requirements of the standard and display the pressure of each tire on the dash 

allowing the driver to diagnose overinflation as well as underinflation. The system’s sensors are most 

often located on the interior end of a tire’s valve stems. Therefore, pressure information must be 

transmitted to the vehicle via a battery-powered radio frequency transmitter. Unlike indirect systems

that do not have sensors in each wheel, direct systems may require additional maintenance and repair

when pressure sensors malfunction due to harsh environmental conditions, depleted batteries or if they 

are removed, replaced or otherwise affected during tire repair or replacement. 

Indirect TPMS: Also known as wheel speed based (WSB), indirect systems use individual wheel rotation 

speeds gathered from the anti-lock brake system (ABS) wheel speed sensors to detect relative 

underinflation. The operating principle is that if a single wheel has a faster rotational speed than other 

tires, then its radius or rolling circumference must be smaller and therefore the tire may be 

underinflated. Because indirect systems produced before FMVSS No.138 were only capable of 

diagnosing underinflation in relation to the other tires on the vehicle, these systems experienced 

difficulty identifying underinflated tires if several tires were simultaneously underinflated. FMVSS No. 

138 required that monitoring systems must be capable of detecting underinflation in ‘one or more of 

the vehicles’ tires, up to a total of four tires’, and as a result most manufacturers began exclusively 

installing direct systems after publication of the final rule. Fewer than three percent of the vehicles 

observed by the TPMS Special Study were equipped with indirect systems. 

Recently some manufacturers have begun to produce indirect systems that integrate other types of data 

from the ABS and electronic stability control (ESC), along with information from other evolving 

technology sensors, into the diagnostic model. At the time of publication there are two vehicle 

manufacturers, Volkswagen and Audi, which utilize indirect systems in production vehicles. The first 

vehicle with an indirect system that met the requirements of FMVSS No. 138 was the model year 2009

Audi A6. In 2010 and 2011 both Audi and Volkswagen began to include indirect systems on several 

models including the Audi A3, A4, A5, A6, A8, Q5, and Q7and Volkswagen’s Golf, GTI, and Jetta. Post-

 3 Code of Federal Regulations, Title 49, Part 571.138
3 

FMVSS No.138 indirect systems may have many practical advantages over direct systems in terms of 

long-term maintenance because there is no physical sensor installed on the wheel and the systems do 

not require independent batteries. Although this survey did not collect sufficient data from these postFMVSS No. 138 indirect systems to evaluate their effectiveness, their performance may be similar to the 

direct systems evaluated in this report (because they also comply with the requirements of FMVSS No. 

138). 

Display Types: To meet the requirements of FMVSS No.138 a vehicle is required to have a warning lamp 

that alerts a driver to severe underinflation as well as a malfunction lamp that indicates when the TPMS 

is not functioning properly. In addition, some vehicles are equipped with tire specific warning lamps 

that tell the driver which tire is triggering the underinflation warning. Other vehicles have tire specific 

inflation pressure readouts that are capable of reporting the current pressure of each tire at any time in 

addition to an underinflation warning lamp. These systems may alert drivers to less severe 

underinflation that has not yet reached the 25% threshold needed to activate the warning lamp. They 

may also prevent overinflation, which is not identified by other types of TPMS displays. Pressure 

displays are only possible with direct TPMS. These display types are evaluated separately in Section 3.4 

(underinflation) and Section 4.4 (overinflation) of this report to explore any possible differences in 

effectiveness. 

Market Share: Figure 1 below shows an estimate of the percent of new vehicle models sold with TPMS 

for model years 2000-2008 (TPMS became mandatory for all new vehicles manufactured on or after 

September 1, 2007). These estimates are based on Ward’s Automotive Yearbook data for 98 popular 

vehicle models.

Figure 1: Estimated Percent of Vehicle Models Sold With TPMS

0

10

20

30

40

50

60

70

80

90

100

Percent

Model Year

Total TPMS

Direct Std

Direct Opt

Indirect Std

Indirect Opt

The steep rise in overall TPMS seen in 2007 is a result of the phase-in schedule specified by FMVSS No. 

138, which required that at least 20 percent of all model year 2006 vehicles and 70 percent of all model 

year 2007 vehicles be equipped with TPMS. The graph also shows that indirect systems were not very 

common before the standard, and that none of the included vehicle models in model years 2006-2008
4

were equipped with an indirect system. (As mentioned above, new designs of indirect systems 

compliant with FMVSS 138 began to appear in model year 2009.)

1.3: Past Research

Tire Pressure Special Study (2001)4 – This study used the 24 primary sampling units (PSUs) located 

throughout the United States established by the National Automotive Sampling System –

Crashworthiness Data System (NASS-CDS). Within these PSUs, drivers were sampled for observation at 

gas stations from February 1, 2001, to February 14, 2001. According to the study, approximately 10

percent of the vehicles on the road in the United States were equipped with TPMS. A total of 11,530 

vehicles were observed and the distribution of observed vehicle types (SUVs, vans, passenger cars, etc.) 

was consistent with national estimates of vehicle registration. There were no restrictions on the model 

years of included vehicles. The primary finding of this study was that 27 percent of passenger cars had 

at least one tire that was underinflated by at least eight psi (which would correspond to 25% 

underinflation for a tire with a common recommended pressure of 32 psi). 

Tire Pressure Monitoring System Study (2009)

5 – This study also used the NASS-CDS infrastructure and 

was specifically designed to analyze the effectiveness of TPMS. This study collected data from a limited 

sample of 2,316 vehicles (data collection was halted before the planned sample size of 12,000 

observations could be realized) from model years 1997 through 2003. The primary findings of this study 

were that most of the surveyed vehicles with correct tire pressure were equipped with TPMS. This 

study also found that 8.4 percent of observed vehicles with TPMS had at least one tire that was more 

than 25-percent underinflated, while 9.9 percent of observed vehicles without TPMS had at least one 

tire that exceeded the same level of underinflation. Most of the TPMS in this study (83%) were indirect 

systems.

1.4: Goals of the Evaluation

The primary goal of this study is to evaluate the effectiveness of TPMS in reducing underinflation in the 

on-road fleet of passenger vehicles, and this evaluation will be the focus of this report. A secondary goal 

of the study is to evaluate the effects of TPMS on overinflation. Although many systems are only 

equipped with a warning light that activates when a tire is underinflated, some systems offer real-time 

tire pressure displayed on the dash. These systems may have an effect on the rate of overinflation 

observed in vehicles equipped with them. 

The third goal of this study is to estimate the observed improvement, if any, in fuel economy resulting 

from the presence of TPMS. Fuel savings resulting from increased tire pressure for vehicles with TPMS 

can at least partially defray the initial cost of the system.

 4 Bondy, N., Thiriez, K. (2001), Tire Pressure Special Study Vehicle Observation Data. (Report No. DOT HS 809 317), 

Washington, DC; National Traffic Safety Administration.

5

 Singh et al. (2009), Tire Pressure Maintenance – A Statistical Investigation. (Report No. DOT HS 811 086), 

Washington, DC; National Traffic Safety Administration.
5

The TPMS-SS survey also included driver interview items to measure driver knowledge about the 

importance of proper tire inflation and to identify the methods by which drivers are informed about

issues pertaining to tire pressure maintenance. Subsequent analyses may be conducted to explore this 

interview data. This information could then inform data-driven and targeted behavioral programs that 

aim to promote proper tire inflation.

2: Methods

2.1: Survey Design

The NASS-CDS6 infrastructure was used for data collection. This infrastructure offers several advantages 

and efficiencies for a nationally representative survey. The primary sampling units (PSUs) have already 

been selected and their weights have been computed. There are full-time data collectors at each PSU 

who are familiar with the area and are highly trained in interview techniques as well as data collection, 

data entry, and quality assurance. These factors allowed for a larger sample size to be collected within a

set budget. The proposed survey design and the data collection forms were submitted to the Office of 

Management and Budget for approval, the approval number is 2127-0626.

Each of the 24 NASS-CDS PSUs were included in the study. Within each of the PSUs, ZIP codes were 

randomly selected for observation with equal probability of selection. Within each selected ZIP code, 

gas stations were first screened, based on inclusion criteria such as traffic flow, location, number of 

pumps, and presence of an attached convenience store in order to ensure that adequate data could be 

collected. Then, stations were selected from those meeting the inclusion criteria (this stage of selection 

was based on traffic flow and researcher convenience). Usually, two gas stations were selected per ZIP

code and seven ZIP codes per PSU for a total of 14 gas stations within each of the 24 PSUs. Once a gas 

station was selected, a data-collection team consisting of an observer and an interviewer spent one day 

(eight hours) sampling vehicles that entered the station. Only vehicles of model year 2004 or newer 

were approached for inclusion due to the rarity of TPMS in older vehicles and to ensure that the number 

of sampled vehicles equipped with TPMS would be sufficient for statistical analysis.

In total, data from 6,503 passenger vehicles from model years 2004-2011 were collected. To be 

included in effectiveness estimates the minimum completeness requirements were measurements of 

pressure and temperature of all four tires, observed presence of TPMS system and type as well as

vehicle make, model, and model year (used to confirm observed TPMS information). A total of 6,103 

vehicles met these completeness requirements and were included in the following analyses of

effectiveness. 

As with any survey, the accuracy and reliability of the estimates derived in this report depend largely on 

the amount of data collected and the magnitude and variance of the sampling weights imposed by the 

sample design. Even with the large overall sample, if there are very few observations in any of the 

 6 Additional information about the NASS-CDS PSUs can be found in Appendix “F” of the 

2010 NASS-CDS Analytical User’s Manual, which is located at the following Internet 

address: http://www-nrd.nhtsa.dot.gov/Pubs/NASS10.pdf. 
6

subpopulations analyzed then the estimates derived may be unstable, non-significant, or otherwise 

misleading. The following tables give the unweighted counts of observations. The counts are separated 

into model year groups: 2004-2007, 2008-2011, and overall total. These model year groups are 

important because some of the evaluations in this report only use data from model years 2004-2007 in 

order to minimize an important possible source of bias due to vehicle age.

Table 1: Counts of Observations

Overall MY 

2004-2007

MY 

2008-2011

Total

All Vehicles 3,297 2,806 6,103

Vehicle Type

Passenger Car 1,565 1,643 3,208

Light Truck/van 1,732 1,163 2,895

TPMS

With TPMS 1,585 2,806 4,391

Without TPMS 1,712 0 1,712

TPMS Type

Indirect 209 0 209

Direct 1,376 2,806 4,182

TPMS Display Type

Warning Lamp Only 1,248 2,031 3,279

Tire Specific Lamp 135 225 360

Tire Specific PSI 202 495 697

Unknown 0 55 55

Improper Inflation

≥ 25% Underinflation 534 150 684

≥ 25% Overinflation 412 265 677

 

These counts are unweighted; however, all of the effectiveness estimates in the report were derived 

using weighted data.

2.2: Site Cooperation

Within each of the 24 NASS-CDS PSUs data were collected from August 2010 to April 2011. Cooperation 

of the owners of the gas stations at the different data collection sites was established prior to the 

commencement of the study mostly via in-person visits by NASS researchers. In addition, a letter 

explaining the scope of the survey was provided to the managers of the participating gas stations on the 

day of data collection. If the researchers were unable to collect data at a pre-authorized site due to 

unexpected circumstances, the next gas station listed for that ZIP code was substituted as an alternate 

site. At the conclusion of the data collection for the day, managers of the gas station were thanked and 
7

given a copy of the informational hand-outs that had been provided to the participants. Later, a thankyou letter was sent to the managers of the gas stations that participated in the study. 

2.3: Survey Participants

The study’s focus was on vehicles and their drivers, although some information was also collected on

other passengers in the vehicles. Both the Tire Pressure Interview Form and the Refueling Interview 

Form (the survey forms are included in the appendix of this report) were used to screen and confirm 

that the vehicle’s model year was 2004 or later. If the vehicle was not, the driver was thanked and the 

interview was ended.

To be included in the study a vehicle had to fall within one of four body types (i.e., passenger cars, utility 

vehicles, vans, and pickup trucks), the vehicle must have had a Gross Vehicle Weight Rating of less than 

10,000 pounds, and the vehicle’s model year must be 2004 or later. Vehicles without VINS or those with 

dual wheels on an axle were excluded as well as vehicles which did not enter the gas station to refuel. 

Some factors that did not affect whether a vehicle was selected for inclusion in the study were whether 

the driver: 1) drove a vehicle with TPMS, 2) was the vehicle’s primary driver, or 3) was the person 

responsible for the vehicle’s maintenance. On the other hand, only drivers of vehicles who were aware 

that their vehicle had TPMS were asked to answer the questions in the Supplemental Form.

If the researcher judged a vehicle to meet the inclusion criteria, the vehicle was approached after it had 

stopped at the gas pump. The researcher would give the driver a letter of introduction and ask the 

driver to participate in the study. Once a driver agreed to participate, one researcher interviewed the 

driver, recording information on the interview forms, while the second researcher inspected the vehicle 

and recorded data on the Vehicle Inspection Form and the Tire Inspection Form. Spanish language data 

collection forms were available, in case the interview could not be conducted in English. At some site, 

Spanish speaking interviewers were also available. At the conclusion of each interview, the participant 

was given a Courtesy Card. This card contained contact information (i.e., a TPMS study email address, 

DOT hotline telephone number) so that the drivers had someone to contact in case they had any further 

questions once they left the study site. Also included on the Courtesy Card were the air pressure 

measured on each tire, the vehicle manufacturer’s recommended cold tire pressure, and several tire 

safety tips. The participants were also given several brochures with additional information about tire 

safety.

2.4: Data Collection Staff

Field data collection was conducted by two-person teams composed of trained data collectors. The

materials employed included the interview forms, hand-outs for the participants, large sign(s) with 

information about the survey, special equipment (e.g., depth indicators, pyrometers, analogue air 

pressure gauges), DOT identification badges, the procedures manual, and miscellaneous items needed 

to obtain and record data such as clip boards, watches, and digital cameras. In general, one member of 
8

the team was responsible for collecting the interview data while the other was responsible for collecting 

vehicle data such as tire pressure.

2.5: Data Collection Schedule

Data were collected during the day, normally between the hours of 8 a.m. and 5 p.m., but some data 

were collected as early as 7 a.m. or as late as 6 p.m. The majority of the day was spent collecting data 

via interviews with the drivers and inspections of the vehicles; however, during each day there was a 45-

minute lunch break as well as five 15-minute breaks spaced evenly throughout the day, during which

time the researchers collected observational data on the vehicles that entered the gas station in order 

to estimate total traffic at the station for that day. 

At the start of the day, the team would set up a “command center” at the gas station where extra survey 

forms and materials were kept. One or two 3’ by 5’ signs would be prominently displayed so drivers 

would be alerted to the fact that a tire pressure study was being conducted that day at the gas station. 

2.6: Data Collection Equipment

Special Equipment used for data collection included an air pressure gauge to measure tire pressure, a

tread depth indicator to measure tread depth, and a pyrometer to measure tire sidewall temperature

and ambient air temperature. 

The air pressure gauges were tested when received by the Zone Centers. In addition, they were tested 

at least 2 days prior to data collection and again at the beginning of each day’s data collection. The test 

was conducted using the two air pressure gauges assigned to each team on the same tire of a vehicle. If 

the pressures were not within a 1 psi tolerance, they were to notify their Zone Center for immediate 

replacement. If the researcher could not determine which gauge was inaccurate, both gauges were to 

be replaced. If replacement gauges could not be in time for a scheduled day of data collection, data 

collection was to be postponed for that day and rescheduled for another day in the same week.

The pyrometers used in the study were checked against each other prior to each data collection day. If

the pyrometers did not measure the same ambient air temperature (within a tolerance of one degree),

the researchers noted the discrepancy on the Daily Site Form and notified the zone center for further 

direction. 

No problems were noted with any of the equipment used in the study.
9

2.7: Forms and Variables

Data collected during the TPSS-SS included information on the sites at which data were collected, the 

vehicles that stopped to refuel at these sites, and the drivers and passengers of these vehicles. These 

data were recorded on seven data collection forms (see appendix) and were collected via observation, 

inspection and interview.

Observational Data

The observational data was collected on two forms: the Daily Site Form (Tallies & Inspections) and the 

Daily Site Form (Refueling). Every two hours, beginning at the initial time of data collection for the day, 

the data collectors observed, for a period of 15 minutes, the number of 2004-2011 passenger cars and 

light trucks that entered the gas station to obtain gasoline. 

One researcher would tally, by body-type category (i.e., passenger cars, utility vehicles, vans, and pickup 

trucks) and time of day, the number of vehicles entering the gas station to obtain gasoline. In addition, 

the area around the gas station was characterized by the observer as being urban, suburban or rural in 

nature. This observational data was recorded in the Daily Site Form (Tallies & Inspections) and was used 

to explore non-response bias at the site and vehicle levels.

The other researcher would log the time it took for vehicles to refuel, by body type and time of day. In 

addition, the observer recorded: 1) the other activities in which the drivers engaged, such as going into 

the gas station store or putting oil into the vehicle, 2) characteristics of that particular station, such as 

type of payment used and additional services offered at this location (e.g., car wash, auto repair), and 3) 

whether the observation period ended before a specific driver had finished refueling. This observational 

data was recorded in the Daily Site Form (Refueling). 

Inspection Data

The inspection data were collected on two inspection forms: the Vehicle Inspection Form and the Tire 

Inspection Form. Both forms were completed by one NASS researcher while another researcher 

conducted an in-person interview with the driver. 

The Vehicle Inspection Form was used to obtain information about the vehicle (e.g., make, model,

and model year) and the type of display (i.e., warning light only, tire specific warning icons, or tire specific 

display of psi) that was used to provide TPMS information to the driver. In addition, the tire-specific psi 

values seen in the TPMS displays, as well as information from the vehicle placard (i.e., recommended tire

size, recommended air pressure, and the Gross Vehicle Weight Rating) were recorded on these forms.

The Tire Inspection Form was used to obtain information about all four of the vehicle’s tires, including 

specifics about the tires (i.e., manufacturer, model, size, and recommended maximum pressure) and 

details about the condition of the tires at the time of the observation (i.e., measured temperature, 

pressure, and tread depth). In addition, the ambient air temperature and general weather conditions 

were recorded on the form.
10

Interview Data

Interview data were collected on three interview forms: the Tire Pressure Interview Form, the Refueling 

Interview form, and the Supplemental Form. Drivers of eligible vehicles were asked the questions on 

the interview forms, while only drivers of vehicles with TPMS were asked to answer the questions in the 

Supplemental Form. 

The Tire Pressure Interview Form was used to obtain information regarding the history of the vehicle 

and its tires, as well as how air is added to the vehicle’s tires (e.g., by whom, when, for what reasons). In 

addition, driver profile data was recorded, as well as driver knowledge about how to keep proper tire 

pressure and where/how the driver obtained information about tire care and tire pressure.

The Refueling Interview Form was used to obtain information regarding the driver’s refueling habits, as 

well as characteristics of vehicle’s occupants (e.g., number, reason for traveling). 

The Supplemental Form was used to obtain information regarding the drivers’ knowledge of their TPMS 

(e.g., location and purpose of the warning lamp and the malfunction lamp, how to reset the lamps, and 

what maintenance, if any, service has been required of the TPMS). Drivers could select one of four ways 

to complete the questions in the Supplemental Form (i.e., an on-site interview, a mail-back 

questionnaire, an online questionnaire, and a call-back interview) but almost all drivers who agreed to 

participate selected the on-site interview.

2.8 Questionnaire Development and Training

Development and Pilot Testing of Data Collection Forms

Data collection forms were developed in consultation with NHTSA subject matter specialists for tires, 

tire pressure, and fuel economy. Initial testing of the survey forms was completed informally within the 

agency, with later testing completed by the NASS Zone Centers during a pre-pilot test in May of 2010.

Recruitment of Field Staff

Field data collection was conducted through the infrastructure of the National Automotive Sampling 

System Crashworthiness Data System (NASS-CDS), which has teams of researchers located at Primary 

Sampling Units (PSUs) throughout the United States. Members of the TPMS-SS data collection teams 

were selected from these researchers, many of whom had experience conducting the prior tire pressure 

studies – the 2001 Tire Pressure Special Study (TPSS) and the 2009 Tire Pressure Monitoring System 

Study (TPMSS). Each PSU had two or more staff persons who participated in the study in teams of two 

researchers. One of the researchers, normally the most experienced staff member at the PSU, served as 

the team leader, and was able to use prior experience to assist other staff members in their data 

collection. After the data collection on each participant was completed, the team leader was 

responsible for the survey forms being complete, accurate and legible. In addition, the team leaders 

were responsible for reviewing collected data against the digital images taken (e.g., the vehicle VIN).
11

Training and Pilot Testing of the Data Collection Protocols

A PowerPoint preview of the TPMS-SS study was presented to the NASS Zone Centers and PSU staff at 

the NASS annual training in December of 2009. In 2010, they participated in two different webinar 

trainings: 1) June training on the application that had been developed for use in entering the TPMS-SS 

survey data, and 2) July training on the survey forms and data collection protocols. After training was 

completed, a pilot test of the TPMS-SS survey forms and data collection protocols was conducted in 

each PSU. Data obtained from the pilot tests were entered into the application, in order to double 

check the data entry application and the survey forms, as well as provide experience with entering the 

survey data into the data entry application. In addition, a data collection procedures manual was 

distributed for reference. Since data collection continued into 2011, another PowerPoint presentation 

on TPMS-SS was made at the NASS annual training in December of 2010.

Unannounced Site Visits

NHTSA and Zone Center staff paid unannounced site visits to monitor the quality of data collection in 

the PSUs.

2.9: Data Entry and Quality Control

Data Entry

Data from the seven paper forms used in the survey were entered manually by the data collectors 

into an application developed specially for the TPMS-SS survey. This data application contained 

automated edit checks, skip patterns and other features to help insure that the data were entered 

correctly. In addition, staff at the Zone Centers checked the data that were entered, including checking 

the images that had been taken. 

Quality Control

After the data were entered, checks were run by NHTSA staff to identify outliers, discrepancies between 

two similar variables, and other such inconsistencies via automated logic checks and data runs. While 

information about data elements that flagged these edit checks was sent to the Zone Centers to be 

reviewed and, if necessary, corrected, no statistical editing was performed to alter the recorded values of

outliers.

After data reconciliation, a final file was translated into SAS data sets. In addition, database 

reconciliation of these final SAS data sets was conducted.

2.10: Weighting and Estimation

The PSU-level weights have been previously established by the NASS-CDS and reflect diverse factors 

such as urbanization and population demographics. The weights at the remaining levels of sampling (ZIP

code, gas station, and vehicle) were computed as simple random samples with the final weights 
representing the inverse of the probability of unit selection in the study.7

  For example, if the PSU had 70

ZIP codes and seven were selected, each selected ZIP code would have been weighted tenfold.  

However, it should be noted that logistical concerns precluded a purely simple‐random selection

procedure at certain stages, precluding perfect national representation.  The selection of gas stations

within PSUs was not fully randomized in order to ensure that selected stations had necessary facilities

and adequate vehicle traffic.  Similarly, vehicles could only be observed during certain hours of the day

and only at gas stations.  Practical restrictions such as these were presumed to introduce no bias, but we

cannot be certain that this is the case.      

Estimation was conducted using SAS survey procedures to specify the experimental design.  The

resulting estimates can be considered nationally representative during the period of data collection

(August 2010 – April 2011).  It is important to bear in mind that these estimates represent only the

population of vehicles that were eligible for inclusion in the survey (for example, all data collection was

conducted between 7 a.m. and 6 p.m.; vehicles that always refuel outside of this interval were not in the

sampled population).  It is assumed that the general population will not differ substantially from the

sampled population.

2.11: Data Corrections and Adjustments

Prior to analysis several corrections were applied to the raw data.  Data were collected on gas stations

and vehicles that refused to participate in the study for the purpose of a non‐response bias analysis,

which revealed no strong bias introduced by non‐response at the gas station or vehicle levels.  No non‐

response bias adjustments were made to the final weights.  A correction to the observed tire pressures

was made to account for tire temperature (measured in degrees Fahrenheit at the outside surface of the

tire) using the Ideal Gas Law and estimates of average tire volume, as well as average ambient

temperatures.  This correction took the form of:

Equation 1: Te

ݏ݁ݎ݌	݁݀ݐݏݑ݆ܽ݀

This correction

correction stan

mperature Adjustment to Observed Tire Pressure

݌݁݉ݐ	݁݀ݒݎ݁ݏܾ݋ሾሺ െ ݁ݎݑݏݏ݁ݎ݌	݁݀ݒݎ݁ݏܾ݋ ൌ ݁ݎݑݏ

was necessary because of the large influence of temperature on tire pressure.  This

dardizes the observed pressures relative to a fixed average ambient temperature (65

degrees Fahrenheit) in order to minimize any potential bias caused by tire temperature.  For example,

consider a rural gas station near a freeway.  It is possible that the vehicles visiting the station will fall

into two general categories: those passing through on the freeway that are more likely to have very hot

tires and TPMS, and local vehicles that will have colder tires and are less likely to have TPMS.  The

correction described above prevents any bias that could be introduced by such a situation.  Because the

െ 65ሻ ∗ .1ሿ

                                                            

12 

7 There is fairly large variability in the sampling weights generated by the complex sampling design.   The smallest observation

weight is 22.3, while the largest is 17837.6.  The mean of the weights is 998.5 and the standard error of the mean is 13.5.  

Although the variability is large, a sensitivity analysis using trimmed weights did not yield substantially different results from the

untrimmed weights.  Based on these results, the untrimmed weights are used for all statistical analyses in the report.
13

same correction is applied to both the treatment (TPMS) and control (no TPMS) vehicles the exact value 

of the average ambient temperature chosen should be inconsequential to the effectiveness estimates. 

The TPMS-SS includes data from vehicles of model year 2004-2011. Vehicles that did not fall within this 

range were not approached for inclusion in the survey. In the primary analyses of TPMS effectiveness 

the vehicle model year range is restricted to 2004-2007 because all vehicles of model year 2008 and 

newer are equipped with TPMS and from that point on direct comparisons of pre- and post-standard 

vehicles of the same model year are impossible. 

In addition to the preceding adjustments the data were checked for internal consistency, for example by 

checking to see that the observed type of TPMS agreed with the available options on the specific make 

and model of vehicle. In instances where variables showed disagreement, the discrepancy was rectified 

using other collected variables when possible and if this was not possible the conflict was resolved by 

forcing agreement with the ostensibly more reliable of the two conflicting variables. 

3: Underinflation

3.1: Summary

Based on the results of this survey, it is estimated that 71.1 percent of all passenger vehicles of model 

year 2004-2011 in the United States have at least one tire that is underinflated by at least one psi 

relative to the recommended pressure, and that 12.4 percent have at least one tire that is severely 

underinflated (more than 25% below the vehicle manufacturer’s recommended cold tire pressure). The 

percentage of model year 2004-2007 passenger vehicles with at least one tire severely underinflated 

was 23.1 percent for vehicles without TPMS and 11.8 percent for vehicles with direct TPMS. Based on 

these rates, TPMS is estimated to be 55.6-percent effective at preventing severe underinflation.

Significant differences were found for different types of TPMS warning displays (single warning lamp, 

tire-specific warning lamp, or tire-specific real-time pressure readout). However, these differences may 

be due to a vehicle model bias rather than to genuine differences in effectiveness among the different 

display types, given that vehicle models with tire-specific and pressure readout displays are likely to be 

different (e.g., more expensive) than models with a single warning lamp.

TPMS was also found to be about 14 percentage points more effective in preventing severe 

underinflation in light trucks and vans (LTVs) than in passenger cars (PCs). There were no differences in 

effectiveness for less severe levels of underinflation by vehicle type.

3.2: Population Estimates

Underinflation is measured at the vehicle level and is defined by the lowest inflation pressure of any tire 

on a vehicle compared to the manufacturer’s recommended cold tire pressure. Under FMVSS No. 138 a 

TPMS must alert a driver when a tire’s pressure is more than 25 percent below the manufacturer’s 

recommended pressure. Underinflation at or below this threshold established in the standard will be 

referred to in this report as ‘severe underinflation’. Because negative safety effects of underinflation 
could also result from lesser degrees of underinflation, several alternative levels of underinflation will

also be considered.  Underinflation of 10 percent or more is referred to as ‘moderate underinflation’,

while underinflation greater than 0 percent is referred to as ‘any underinflation’.  Measurements of tire

pressure were taken with precision of one psi.

Analyses of underinflation are done at the vehicle level.  The least inflated tire after temperature

correction is selected to represent the vehicle.  Figure 2 below shows the weighted distribution of this

least inflated tire for the 6,103 vehicles in the study from which researchers were able to collect the

following information: pressure and temperature of all four tires, make, model, and year of vehicle, and

presence and type of TPMS.  Once weighted, the vehicles included in Figure 2 below represent

6,108,266 vehicles in the United States.  The application of weights accounts for factors such as

demographic make‐up and geographic region, and result in a nationally representative set of data.  The

positive range on the right side of the graph represents vehicles on which all four tires are inflated above

the recommended level.

14 

Figure 2: Distribution of Least Inflated Tire – All Surveyed Vehicles

The mean of the distribution shown above is ‐6.626 percent (6.626 percent below manufacturer’s

recommended cold pressure) and the median is ‐5.667 percent.  The underinflation rates for several

cutoff points along with their standard errors are reported in Table 2 on the following page.  The first

row in the table reflects the overall distribution of surveyed vehicles shown above.  The other rows

represent the findings for specific subsets of the survey sample.
Table 2: Weighted Population Underinflation Rates

Any

Underinflation

At Least 10%

Below

At Least 25%

Below  

All Surveyed

Vehicles 71.1% (±2.1%) 41.3% (±2.4%) 12.4% (±1.5%)

Vehicle Type

   Passenger   

   Cars 70.0% (±2.2%) 39.9% (±2.4%) 12.2% (±1.5%)

   Light    

   Trucks/Vans 72.3% (±2.4%) 42.7% (±2.5%) 12.6% (±1.6%)

TPMS

No TPMS  

(MY 2004‐2007) 73.5% (±2.4%) 53.9% (±2.7%) 23.1% (±2.2%)

Direct TPMS

(MY 2004‐2007) 73.7% (±2.6%) 43.1% (±3.1) 11.8% (±2.3%)

Direct TPMS

(MY 2008‐2011) 68.4% (±3%) 32.2% (±2.7%) 5.7% (±1%)

Direct TPMS

(MY 2004‐2011) 70.2% (±2.7%) 36% (±2.8) 7.8% (±1.4%)

15 

There is a clear difference in the rates of severe underinflation for surveyed vehicles with and without

TPMS.  The difference for moderate underinflation is much more modest, and there appears to be little

difference for any underinflation.  There does not appear to be much of an effect for vehicle type,

although LTVs appear to have slightly higher rates of underinflation.   

Comparison of these rates with past research is difficult.  The Tire Pressure Special Study (2001) was

conducted before FMVSS No. 138 was proposed and the population results are reported in absolute psi

rather than percent difference between observed and recommended pressure.  The Tire Pressure

Monitoring System Study (2005) only reported the proportion of vehicles with severely underinflated

tires that were TPMS‐equipped, not the proportion of vehicles that had severely underinflated tires.   

3.3: TPMS Effectiveness

To evaluate the effectiveness of TPMS, the collected survey data were restricted to the model years

2004‐2007.  These were the only model years that contained vehicles both with and without TPMS and

by restricting the analysis to these model years it is less likely that bias was introduced by having

substantially different types of vehicles in the comparison groups (vehicles with and without TPMS).  It is

also likely that there is a correlation between proper inflation rate and model year, particularly for

newer vehicles which will be more likely to have properly inflated tires.  Restricting included model

years should also attenuate any bias introduced by the interaction between the effects of vehicle age on

the likelihood of underinflation and the likelihood of the presence of TPMS.   
16

All vehicles with indirect TPMS systems were removed from the overall effectiveness analysis, although 

they are considered separately in section 3.4. These systems were removed because they were all 

earlier indirect systems that are not capable of meeting the current standard and are unlikely to 

represent the effectiveness of either direct systems or the more advanced indirect systems that do meet 

the requirements of FMVSS No. 138. 

The graph below shows the ratio of vehicles surveyed with one or more tires that falls below three 

different threshold levels of underinflation: more than 0 percent (any underinflation), more than 10

percent (moderate underinflation), and more than 25 percent (severe underinflation). Vehicles are 

plotted across model year and grouped by presence of TPMS.

Figure 3: Population Underinflation Rates by Model Year and TPMS

The graphical analysis of the data shows that vehicles with TPMS are less likely to have one or more tires 

that are moderately or severely underinflated. Notice there is little or no difference between TPMSequipped and non-equipped vehicles in the left graph showing the rate of “any” underinflation, not a 

surprising result given that most TPMS systems will only alert a driver to underinflation if it is 25 percent

or worse. 

Another important finding of these data is that while underinflation rates among vehicles without TPMS 

seem to be very stable across model year, vehicles with TPMS show a consistent decline in 

underinflation rates as model year increases. There are several plausible explanations for this observed 

increase in effectiveness. It is possible that TPMS technology improved from 2004 to 2010 (for example, 
17 

Figure 1 shows that direct systems became much more common relative to indirect systems over this 

period). It is also possible that TPMS suffers from attrition when malfunctioning units are not repaired 

or when the systems are not recalibrated after sensors are replaced. A third hypothesis is that drivers 

may pay less attention to the messages from TPMS as the vehicles age. The population rates of 

underinflation for MY 2004-2010 vehicles equipped with TPMS are shown in Figure 4 below.

Figure 4: Population Underinflation Rates: All Surveyed Vehicles With TPMS

The trend toward decreasing underinflation rates as model year increases seen in Figure 3 clearly 

continues through 2010, but with the data available it is impossible to determine the underlying causes 

of this linear trend. By averaging across model years 2004-2007 to create an effectiveness estimate, the 

resulting estimate will be an accurate depiction of the observed effectiveness in the sampled population

of MY 2004-2007 vehicles, but because of the trend it will likely be a conservative estimate

(underestimate) of the true effectiveness of adding a TPMS to a new vehicle. Conversely, if the 

hypotheses of system attrition or reduced driver response are correct, it could be an overestimate of the 

future effectiveness in older vehicles.

Effectiveness estimates are derived by computing the relative risk of underinflation for vehicles with 

TPMS versus vehicles without TPMS. This method compares the likelihood that a vehicle without TPMS 

will have at least one underinflated tire to the likelihood that a vehicle with TPMS will have at least one 

underinflated tire. If the probability of underinflation is the same for the two groups, then the relative 
risk will be one and the percent effectiveness will be zero.  If the incidence is less for vehicles with TPMS

than for vehicles without TPMS, then the relative risk will be less than one.  The relevant formulae for

risk ratio and percent effectiveness are given below; all statistics used are weighted and corrected for

sampling design.  

Equation 2: The Risk Ratio

18 

ܯܶܲ	/ݓ	ݏ݄݁ݒ	݁݀ݐ݂݈݅݊ܽݎ݊݀݁ݑ	݂݋	# ൬ 	 ൌ ݋݅ݐܽݎ	݇ݏ݅ݎ

ܶ	/ݓ	ݏ݄݁ݒ	݁݀ݐ݂݈݅݊ܽ	ݕ݈ݎ݁݌݋ݎ݌	݂݋	#

ܵ

ܶܲ	݋/ݓ	ݏ݄݁ݒ	݁݀ݐ݂݈݅݊ܽݎ݊݀݁ݑ	݂݋	# ൬ ܵܯܲܵܯ ൘ 	݋/ݓ	ݏ݄݁ݒ	݁݀ݐ݂݈݅݊ܽ	ݕ݈ݎ݁݌݋ݎ݌	݂݋	#

ܶܲܯܵ ൰ ൰

Equ

ݐܿ݁݊ݎ݁݌

ation

ݏݏ݁݊݁ݒ݅ݐ݂݂݁݁ܿ	

3: Percent Effecti

ൌ

v

ሺ

eness

ݎെ1

In the preceding equations ‘underi

݅

nflate

ܽݎ	݇ݏ

d’

݋݅ݐ

and

ሻ ∗ 1

‘properly

00

inflated’ are determined by the threshold of

interest.  For example, when evaluating effectiveness at preventing underinflation of 25 percent or

more, a vehicle whose least inflated tire is 20 percent below the manufacturer’s recommended pressure

would be considered properly inflated.   

Table 3 below gives the effectiveness estimates and their 95‐percent confidence intervals for the three

underinflation thresholds shown in Figure 3 above.  Estimates and confidence intervals are computed

using SAS PROC SURVEYFREQ, which accounts for sample design when estimating variance.  The percent

effectiveness estimates may be interpreted as the reduction in likelihood of underinflation that results

when a vehicle is equipped with TPMS.

Table 3:  TPMS Effectiveness Estimates (MY 2004‐2007)

Any Underinflation > 10%

Underinflation

> 25%

Underinflation

Percent

Effectiveness ‐1% 35.3%* 55.6%*

95% Confidence   

Interval (‐45.8%, 29.7%) (13.4%, 51.6%) (36%, 69.2%)

*=statistically significant at p<.05

TPMS is estimated to be 56‐percent effective at preventing severe underinflation of 25 percent or more

below the manufacturer’s recommended cold tire pressure.  It is also estimated to be very effective

(over 35%) at preventing moderate underinflation of 10 percent or more.  It does not appear to have an

effect on mild underinflation, which includes any underinflation whatsoever.   

3.4: Effectiveness by Display and System Type

The analyses of display type and system type will differ slightly from other analyses in this report

because of unequal sample size.  Different statistical methods are necessary because of the large

inequality in sample sizes that would result from an analysis of overall effectiveness.  For example, if
19

vehicles with tire-specific PSI displays were compared to vehicles without TPMS there would be very few 

of the former and thousands of the latter. This inequality could lead to spurious statistical conclusions, 

namely that small differences may be found statistically significant because of the large size of the 

control group (vehicles without TPMS). Instead of using risk ratios the estimates in this section and the

following section on PC/LTV effectiveness are rates of underinflation within the analysis groups. This 

means, for example, that vehicles with tire-specific PSI readouts are compared to standard systems with 

only a warning lamp, rather than vehicles without TPMS. A logistic regression is then used to determine 

if these differences are statistically significant. This method of analysis will be less sensitive to unequal 

sample sizes. 

Display Type: Because there are relatively few observations of vehicles with tire-specific warning lamps 

or tire-specific PSI displays, it is difficult to estimate the overall effectiveness individually for these 

separate technologies. However, it is possible to gain some insight into relative effectiveness by 

comparing underinflation rates in all vehicles with TPMS by TPMS display type: warning lamp only, tirespecific warning lamp, and tire-specific PSI display. This allows the entire range of collected model years 

(2004-2011) to be included because the comparison is made within vehicles with TPMS, not between 

vehicles with TPMS and vehicles without TPMS.

A one-way ANOVA showed a significant effect of display type on the amount of underinflation observed 

in vehicles with TPMS (vehicles without any type of TPMS or with indirect TPMS were removed from this 

model). Table 4 below shows the weighted severe underinflation rates for the different TPMS display 

technologies.

Table 4: Rate of Severe Underinflation by TPMS Display Type (MY 2004-2011)

TPMS Display Rate of Severe

Underinflation 

95% Confidence 

Interval

Warning Lamp 

Only 7.93% (5.04%, 10.81%)

Tire-Specific 

Warning 11.67% (4.83%, 18.51%)

Tire-Specific

PSI 5.67% (2.71%, 8.63%)

Vehicles with tire-specific inflation pressure displays have the lowest rate of severe underinflation. 

Unexpectedly, vehicles with tire-specific warning lamps appear to have a higher rate of severe 

underinflation than standard systems with a single underinflation warning lamp. A logistic regression 

designed to test the significance of this difference while controlling for vehicle age and vehicle type (PC 

or LTV) showed that both differences are statistically significant. Based on the percentages in the table 

above systems with tire-specific PSI readouts are 40 percent more effective than standard warning lamp 

only systems at preventing severe underinflation, while systems with tire-specific lamps are 32 percent 

less effective. These differences are statistically significant (p = .004 and p = .007, respectively). 
20

It is important to note that in the primary analysis of overall TPMS effectiveness, every effort was made 

to ensure that the vehicles being compared were similar to one another. This was done by restricting 

the model year range of vehicles, and the assumption of vehicle model homogeneity was checked by 

restricting the makes and models included in a supplemental analysis to only vehicles that were 

produced both with and without TPMS (see section 5.2: Possible Sources of Bias). The same efforts 

could not be made here because of the limited sample size and as a result, the types of vehicles being 

compared may be dissimilar and this may be driving the observed differences rather than the type of 

TPMS display. Vehicle models with tire-specific warning lamps and PSI displays are likely to be more 

similar to one another (more expensive, more performance-oriented, etc.) than to vehicle models with 

only a single warning lamp. Until more data can be collected on vehicles with tire-specific TPMS displays 

it will be difficult to separate differences due to vehicle model from differences due to TPMS display 

type.

System Type: In this survey the only observed vehicles with indirect TPMS (n=209) were produced prior 

to FMVSS No. 138, and therefore the following analysis is restricted to model years 2004-2007. More 

recent indirect systems that meet the requirements of FMVSS No. 138 may have similar effectiveness to 

direct systems, but this will not be possible to verify until there are enough vehicles on the road to 

provide supporting data.

Table 5: Rate of Severe Underinflation by System Type (MY 2004-2007) 

System Type Rate of Severe 

Underinflation

95% Confidence 

Interval

Direct 11.78% (7.06%, 15.52%)

Pre-FMVSS No. 138 

Indirect

15% (7.57%, 22.42%)

The direct systems in the survey have a slightly lower weighted rate of severe underinflation than the 

indirect systems. Based on these observed rates, severe underinflation is 21 percent less common in 

vehicles with direct systems than in vehicles with indirect systems. Both a Chi-square analysis of the 

unweighted data and a logistic regression of the weighted data gave similar results (p=.09) that suggest 

that this difference is marginally significant. It should be noted that these results do not apply to more 

recent advanced indirect systems that meet the requirements of FMVSS No. 138. 

3.5: Effectiveness by Vehicle Type

As noted earlier in this report, there is a linear effect on the effectiveness of TPMS over time, with 

newer TPMS systems showing lower underinflation rates than older TPMS systems (see Figure 4). If 

there is a linear effect of time on the ratio of passenger cars (PCs) to light trucks and vans (LTVs) as well 

as a large difference in underinflation rates between these two groups, then this could create a source 

of bias in the overall effectiveness estimates. Although Table 2 shows that in the overall sample there is 

a negligible difference in underinflation rates for PCs and LTVs, a larger difference may exist in the older 
21

2004-2007 vehicles included in the effectiveness analysis which could result in a directional bias. This 

analysis may also shed some light on the linear trend in effectiveness of TPMS systems over time 

illustrated by Figure 4. Figure 5 below shows the relative frequency of PCs and LTVs by model year in 

the vehicles observed by the TPMS-SS survey.

Figure 5: PC/LTV Frequencies by Model Year

There appears to be a moderate trend towards a higher proportion of passenger cars relative to LTVs as 

model year increases. The trend appears especially strong during the model years included in the 

effectiveness evaluation, 2004-2007. This trend towards relatively fewer LTVs is also found in vehicle 

registration data from R.L. Polk. According to Polk, registrations in calendar year 2010 ranged from 46 

percent PCs in model year 2004 to 51 percent PCs in model year 2007. These numbers are very similar 

to those observed by TPMS-SS collection staff.

Effectiveness by vehicle type was evaluated using the same statistical methods as the overall evaluation 

of effectiveness (see section 3.3: TPMS Effectiveness). Table 6 below shows the effectiveness of TPMS 

at preventing severe underinflation by vehicle type. The model years included (2004-2007) are the 

same as for the overall analysis.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2004 2005 2006 2007 2008 2009 2010 2011

Model Year

LTV

PC
22

Table 6: TPMS Effectiveness by Vehicle Type (MY 2004-2007)

Passenger Cars Any 

Underinflation

> 10% 

Underinflation

> 25% 

Underinflation

 Percent Effectiveness

 Of TPMS 14.2% 33.7%* 47.3%*

 95% Confidence 

 Interval (-27%, 42%) (17.6%, 46.7%) (22.7%, 64.1%)

Light Trucks/Vans

 Percent Effectiveness

 Of TPMS (95% CI) -14.5% 37.7%* 61.2%*

 95% Confidence 

 Interval (-111.8%, 38.1%) (3.3%, 59.9%) (39.9%, 75%)

*=statistically significant at p<.05

There is a modest difference in effectiveness for severe underinflation between vehicle types, with LTVs 

showing higher effectiveness (61.2%) than passenger cars (47.3%). Because the relative rate of LTVs is 

decreasing over time, this difference can’t be responsible for the observed increase in TPMS 

effectiveness as model year increases. This moderate difference coupled with the moderate change in 

fleet composition over the included model year period should not be introducing any appreciable bias to 

the overall estimates of TPMS effectiveness, although it suggests that the linear effect of time on TPMS 

effectiveness may be even stronger than suggested by Figure 4, which combines PCs and LTVs. 

4: Overinflation 

4.1: Summary

Although overinflation poses less of an established safety risk than underinflation, it may still have 

negative effects on vehicle stability and tire integrity, wear and traction. These negative effects may be 

accompanied by a non-safety benefit of improved fuel economy. The results of this analysis suggest that 

TPMS is 30.7-percent effective at preventing severe overinflation (more than 25% above the

manufacturer’s recommended cold tire pressure). There were no significant differences in effectiveness 

for different TPMS display types, but LTVs showed a significantly higher effectiveness rate than PCs. 

The use of 25 percent as a threshold value to define severe overinflation is a bit more arbitrary here 

than in the case of underinflation, where research has been conducted on the effects of various levels 

underinflation. In the absence of research capable of informing a more meaningful threshold it is used 

throughout this section as the point that defines severe overinflation. 
23

4.2: Population Estimates

Like underinflation, overinflation is defined at the vehicle level and is analyzed by selecting the most 

inflated tire on a vehicle to represent that vehicle. The same inclusion criteria that were used in the 

analysis of underinflation were applied to the data, resulting in inclusion of the same 6,103 vehicle 

observations. Figure 6 below shows the distribution of the most inflated tire of each of the vehicles 

included. Percent values are relative to the manufacturer’s recommended cold tire pressure and have 

been corrected to reflect the temperature of the tire at the time of measurement. Note that it is 

possible for a single vehicle to be simultaneously severely underinflated and severely overinflated.

Figure 6: Distribution of Most Inflated Tire – All Surveyed Vehicles

Overinflation seems less prevalent than underinflation in general, although most vehicles have at least 

one tire that is above the manufacturer’s recommended cold tire pressure. The mean of the distribution 

is 6.7 percent and the median is 4.7 percent. The overinflation rates for several cutoff points along with 

their standard errors are reported in Table 7 below.
24

Table 7: Population Overinflation Rates (All Surveyed Vehicles)

Any 

Overinflation

At Least 10% 

Above

At Least 25% 

Above

All Surveyed 

Vehicles 58.1% (±2.5%) 34.3% (±2.2%) 11.9% (±1.3%)

Vehicle Type

 Passenger 

 Cars 60.9% (±2.7%) 35.6% (±2.7%) 12.7% (±1.7%)

 Light 

 Trucks/Vans 55.9% (±2.6%) 33.2% (±2.0%) 11.3% (±1.3%)

TPMS

 

 No TPMS 58.0% (±2.7%) 35.6% (±2.2%) 13.5% (±1.7%)

 

 TPMS 58.3% (±2.7%) 32.6% (±2.6%) 10.0% (±1.3%)

These results show that severe overinflation is only slightly less common than severe underinflation. 

There are probably several different factors that are contributing to the high incidence of overinflation. 

There may be unintentional overinflation due to confusion between the recommended tire pressure

posted on the placard inside the driver’s door jamb and the maximum pressure posted on the sidewall 

of each tire or because people are filling their tires by eye without an accurate gauge. There may also 

be some intentional overinflation in order to improve fuel economy. Drivers might also intentionally put 

a little extra air in their tires to assure themselves the tires are not underinflated and to reduce how 

often they need to check or refill the tires. Based on the high observed rate of overinflation it may be 

worthwhile to investigate more closely the effects of overinflation on safety and tire integrity, function 

and wear. 

4.3: TPMS Effectiveness

For the evaluation of TPMS effectiveness in preventing overinflation the included model years were 

restricted to 2004-2007. These model years include vehicles both with and without TPMS, allowing for a 

direct comparison. 

Figure 7 below shows the ratio of vehicles surveyed with one or more tires that fall above three 

different threshold levels of overinflation: more than 0 percent, more than 10 percent, and more than 

25 percent. Vehicles are grouped by model year and by presence of TPMS.
25

Figure 7: Overinflation Rates by Model Year and TPMS

TPMS appears to have no consistent effect on the rate of any overinflation, but some effects on the 

rates of moderate and severe overinflation. In order to quantify these effects and determine if they are 

statistically significant, the same method of statistical analysis used for underinflation (risk ratios and 

associated percent effectiveness) was employed here.

Table 8: TPMS Effectiveness Estimates (MY 2004-2007)

Any 

Overinflation

> 10% 

Overinflation

> 25% 

Overinflation

Percent Effectiveness

-1.5% 13.3% 30.7%*

95% Confidence 

Interval (-22%, 15.6%) (-1.7%, 26.1%) (4.9%, 49.5%)

*=statistically significant at p<.05

This analysis shows that TPMS is significantly effective at preventing severe overinflation. Although 

there is little research into the specific consequences of overinflation, it is likely that overinflation will 

have negative effects on vehicle handling and stability, as well as tire wear and integrity. The decline in 

overinflation attributed to TPMS should be considered a positive associated and unintended benefit of 

the technology.

It is difficult to explain how TPMS systems prevent overinflation. Some TPMS (11.5% of the surveyed 

vehicles with TPMS) give a real-time pressure reading of all four tires as a dash display, and these 

systems would inform the driver of overinflation, although without an associated warning light. But the 
26

effectiveness was found to be equally large if not larger for the far more common systems (88.5% of the 

surveyed vehicles with TPMS) that do not report pressure information and only alert the driver to 

underinflation through a warning light. It is possible that TPMS encourages good tire maintenance in 

general which results in reduced overinflation as well as underinflation. Drivers who habitually 

overinflate their tires as a hedge against underinflation and frequent checking might perhaps be less 

inclined to do so when they know that TPMS will warn them of an underinflation problem. 

4.4: Effectiveness by Display and System Type

Display Type: The same methods used in the corresponding section within the underinflation analysis 

were employed to estimate the relative effectiveness of the different TPMS display types observed in 

the survey. An ANOVA did not show any difference in effectiveness on general overinflation for 

different display types. The same logistic model used in the underinflation analysis was applied here to 

check for any differences in effectiveness on severe overinflation. This analysis also failed to find any 

significant effect of display type, vehicle type, or model year on overinflation rate.

Although it was hypothesized that TPMS with a tire-specific PSI display would be driving the observed 

effectiveness in overinflation reduction, this does not appear to be the case. The rates of severe 

overinflation by display type are shown in Table 9 below.

Table 9: Rate of Severe Overinflation by TPMS Display Type (MY 2004-2011)

TPMS Display Rate of Severe 

Overinflation 

95% Confidence 

Interval

Warning Lamp 

Only 10.53% (7.41%, 13.65%)

Tire-Specific 

Warning 6.7% (1.64%, 11.77%)

Tire-Specific

PSI 11.94% (5.5%, 18.37%)

Notice that the tire-specific PSI displays actually have the highest observed rate of severe overinflation. 

Because the differences are not statistically significant one should avoid drawing any conclusions from 

these differences.

System Type: Exploratory analysis did not suggest that there was a significant effect of system type on 

overinflation rates. The rates for direct and indirect systems are reported in Table 10 on the following 

page.
27

Table 10: Rate of Severe Overinflation by System Type (MY 2004-2007) 

System Type Rate of Severe 

Overinflation

95% Confidence 

Interval

Direct 11.94% (9.27%, 14.61%)

Indirect 14.99% (8.37%, 21.61%)

The small observed difference in severe overinflation rates is not statistically significant.

4.5: Effectiveness by Vehicle Type

TPMS effectiveness by vehicle type was estimated using the methods described in section 3.3: TPMS 

Effectiveness. The results are shown in Table 11 below.

Table 11: TPMS Effectiveness by Vehicle Type (MY 2004-2007)

The only significant result is the reduction in severe overinflation in light trucks and vans. Although 

these findings help to pinpoint where the benefits are taking place, they do not shed much light on how 

TPMS is preventing overinflation. It is also interesting to note that effectiveness increases at the higher 

levels of overinflation. TPMS will not alert a driver to any level of overinflation, and one would expect 

that if the observed effectiveness is caused by driver characteristics then the effect might be about the 

same at all levels of overinflation severity.

Passenger Cars Any 

Overinflation

> 10% 

Overinflation

> 25% 

Overinflation

 Percent Effectiveness

 of TPMS -8.3% 11.4% 12.9%

 95% Confidence 

 Interval (-29.1%, 9.2%) (-21.9%, 35.5%) (-44.6%, 47.5%)

Light Trucks/Vans

 Percent Effectiveness

 of TPMS 1.4%% 14.3% 42.6%*

 95% Confidence 

 Interval (-33%, 26.9%) (-18.8%, 38.2%) (16.3%, 60.6%)

*=statistically significant at p<.05
5: Fuel Economy

5.1: Summary

Although the primary goal of TPMS is to reduce underinflation in order to make vehicles safer to

operate, a further benefit of reduced underinflation is improved fuel economy.  By combining estimates

of reduced underinflation due to TPMS with estimates of increases in fuel economy resulting from

increases in tire pressure, it’s possible to estimate the amount of fuel that TPMS will save an average

vehicle during a given period

$

of time.  During the first eight years of operation TPMS is estimated to save

a typical passenger car 9.32 gallons of fuel and typical LTV 27.89 gallons of fuel.  During 2011 TPMS is

estimated to have saved 511,066,488 across the vehicle fleet.

5.2: Vehicle‐Level Average Underinflation

While the least and most inflated tires on a vehicle are key metrics in terms of vehicle safety, it is

important from a fuel economy standpoint to consider the average underinflation per tire across the

four tires on a vehicle.   The vehicle‐level average underinflation is shown in Table 12 below.   

Underinflation is shown as a negative number and overinflation as a positive number.  For example, for

a vehicle with two tires underinflated by three PSI each and the other two overinflated by one PSI each,

the average underinflation would be [(‐3) + (‐3) + (1) + (1)]/4 = ‐1, or underinflated by an average of 1 PSI

per tire.  This is a meaningful number because of the approximately linear relationship between tire

pressure and fuel economy over the inflation range that was observed in the TPMS‐SS.   

Average underinflation was computed for every complete vehicle observation in the TPMS‐SS and

averaged across vehicle model year.  The results for vehicles with and without TPMS are shown in Figure

8 below.

Figure 8:  Vehicle Average Underinflation  

28 

‐3.5

‐3

‐2.5

‐2

‐1.5

‐1

‐0.5

0

2004 2005 2006 2007 2008 2009 2010

Average Underinflation (PSI)

Model year

No TPMS

TPMS
29

Although TPMS was shown in the previous section to result in decreases in overinflation as well as 

underinflation, vehicles with TPMS show less average underinflation than vehicles without TPMS. The 

difference is fairly consistent, except for the large drop in 2007 vehicles without TPMS. This data point 

represents the smallest sized group on the figure; by model year 2007 most vehicles were equipped 

with TPMS. The small sample size may be creating some instability in the estimate, and this graph 

suggests that it would be prudent to average across model years 2004-2007 to obtain an accurate 

estimate of the effect of TPMS on average underinflation.

When averaged across model years and vehicle types the difference in average underinflation per tire 

between vehicles with and without TPMS is 1.05 psi. The difference for passenger cars is 0.714 psi and 

for LTVs the difference is 1.405 psi. The estimated average total underinflation for vehicles with and 

without TPMS is shown in Table 12 below with the 95-percent confidence interval. 

Table 12: Average Underinflation in PSI

Population Average

Underinflation

95% Confidence 

Interval

All Surveyed Vehicles -0.484 (-1.031, 0.062)

 

 PC -0.179 (-0.673, 0.316)

 

 LTV -0.736 (-1.394, -0.078)

Vehicles Without 

TPMS (MY 2004-2007) -1.406 (-2.252, -0.56)

 

 PC -0.79 (-1.396, -0.184)

 

 LTV -1.948 (-3.174, -0.723)

Vehicles With Direct 

TPMS (MY 2004-2007) -0.35 (-1.088, 0.388)

 

 PC -0.076 (-0.785, 0.632)

 

 LTV -0.543 (-1.456, 0.371)

There is, on the average, less underinflation per tire in TPMS-equipped vehicles because TPMS is quite 

effective in reducing severe underinflation. In computation of the average effect, this reduction in 

underinflation is partly attenuated because TPMS also reduces severe overinflation. Therefore the net 

effect is not as large as it would have been if TPMS only reduced underinflation and had no effect on

overinflation. 
30 

5.3: Effect of TPMS on Fuel Economy

Tire Pressure and Fuel Economy: Several studies have been conducted to compare the relationship 

between tire pressure and fuel economy. Table 13 below is adapted from the report ‘NHTSA Tire Fuel 

Efficiency Consumer Information Program Development’8 and shows the results of several recent 

studies.

Table 13: Past Research on Tire Pressure and Fuel Economy

Study Predicted Increased % 

MPG/PSI Increase

Wicks and Sheets9 0.38

U.S. DOE10 0.3

Clark et al.11 0.1-0.6†

Hall and Moreland12 0.1-0.4†

Continental Tire13 0.16-.032†

U.S. EPA14 .33†

Average 0.308

†= mpg calculated through rolling resistance

Although the analysis methods differed, these reports show strong agreement on the effect of tire 

pressure on fuel economy and the estimates reported are very similar. On average, fuel economy is 

expected to increase by 0.308 percent for every 1-percent increase in average tire pressure. Combining 

this average estimate with the results from Table 12 above allows an estimate of the improvement in 

fuel economy attributable to TPMS.

 

8 Evans, Larry R., et al. (2009) NHTSA Tire Fuel Efficiency Consumer Information Program Development: Phase 2-

Effects of Tire Rolling Resistance Levels on Traction, Treadwear, and Vehicle Fuel Economy. (Report No. DOT HS 811 

154), Washington, DC, National Highway Traffic Safety Administration

9 Wicks, F. & Sheets, W. (1991). “Effect of Tire Pressure and Performance Upon Oil Use and Energy Policy Options.” 

Proceedings of the 26th Intersociety Energy Conversion Engineering Conference IECEC-91, Aug. 4-9, 1991, Boston, 

Mass. Vol. 4, pp 307. La Grange, IL: American Nuclear Society.

10 EPA. (2009). Fuel Economy Guide. Washington, DC: Environmental Protection Agency. [Website]. 

http://www.fueleconomy.gov/feg/maintain.shtml.

11 Clark, S. K. & Dodge, R. N. (1979). A Handbook for the Rolling Resistance of Pneumatic Tires. Prepared for the 

U.S. DOT. Ann Arbor, MI: Regents of the University of Michigan.

12 Hall, D.E. & Moreland, J.C. (2000). Fundamentals of Rolling Resistance. Spring 2000 Education Symposium No. 

47, Basic Tire Technology: Passenger and Light Truck. Akron, OH: American Chemical Society. 

13 Continental Tire. (2008). Government Regulation in Transition-Continental Tire Point of View. Presentation 

before the California Energy Commission.

14 Grugett, B.C., Martin, E. R., & Thompson, G.D. (1981). The Effects of Tire Inflation Pressure on Passenger car Fuel 

Consumption. International Congress and Exposition, Feb. 23-27, 1981. Paper #810069, SAE Technical Paper Series. 

Warrendale, PA: Society of Automotive Engineersm Inc.
Equation 4: Effect of TPMS on Fuel Economy

31 

ߛ ∗ ሻ଴݌ െ ଵ݌ሺ ൌ ሺΔሻ	ݏݏ݁݊݁ݒ݅ݐ݂݂݁ܿܧ

Where is the average underinflation of model year 2004‐20

average underinflation of 2004‐2007 vehicles without TPMS (b

the average predicted percent increase in fuel economy accom

pressure (taken from Table 13 above).  Solving separately for p

ଵ݌

Δ௅்௏ ൌ െ0.543 െ ሺെ1.948ሻ ∗ 0.308% ൌ 0.433%

Δ௉஼ ൌ െ0.076— 0.79 ∗ 0.308% ൌ 0.22%

Although the estimated improvements in fuel economy are modest, over time and across the fleet they

may represent a considerable savings.

Estimated Fuel Savings Per Vehicle:  Using some additional estimates and assumptions it is possible to

calculate the amount of fuel saved by TPMS over the life of a new vehicle.  This quantity is estimated by

calculating the putative fuel economy of a hypothetical model year 2011 vehicle without a TPMS system

and comparing that fuel economy to the known average fuel economy of a 2011 vehicle (all of which are

equipped with TPMS).   

In Table 14 below the survival probability and average miles travelled are reported for the first eight

years of a new vehicle’s life.  Only the first eight years are included because as a vehicle ages it becomes

more likely that its TPMS will be nonfunctioning due to either mechanical failure (for example, all direct

pressure sensors are equipped with a battery) or some other factor.  Placing an upper limit on the

effective lifespan of a TPMS unit should help prevent an overestimation of the benefits provided during

a vehicle’s lifetime.   

All estimates in this chapter are calculated separately for passenger cars and LTVs due to differences

between these two groups in TPMS effectiveness and other characteristics.  

̅

07 vehicles with direct TPMS,   is the

12 above), and   is

rease in average tire

s gives:

଴݌

oth taken from Table ߛ

panying a one psi inc

assenger cars and LTV

̅
Table 14: Estimated Survival Probability and Miles Traveled by Vehicle Age

32 

Passenger cars

Vehicle Age

(Years)

Survival Probability

[ ]

Miles Traveled

࡯ࡼ࢞ ࡯ࡼ ( ) ሻ࢙ሺࡼ

1 0.995 14,231

2 0.990 13,961

3 0.983 13,669

4 0.973 13,357

5 0.959 13,028

6 0.941 12,683

7 0.919 12,325

8 0.892 11,956

Light Trucks and Vans

Vehicle Age

(Years)

Survival Probability

[ ]

Miles Traveled

ࢂࢀࡸ࢞ 	ࢂࢀࡸ ( ) ሻ࢙ሺࡼ

1 0.974 16,085

2 0.960 15,782

3 0.942 15,442

4 0.919 15,069

5 0.891 14,667

6 0.859 14,239

7 0.823 13,790

8 0.783 13,323

The survival probabilities15  and average miles traveled16 are used in the following equation to estimate

the average amount of fuel saved per vehicle. Notice that the summation indices in Equation 4 below

are from two to eight; an assumption of this analysis is that TPMS will have no effect on tire pressure for

the first year of operation.  The survey data showed very little underinflation in vehicles that were

produced in the same year as data collection.

                                                            

15 The survival rates were calculated from R.L. Polk, National Vehic

dules, Office of

olk‘s NVPP is an

ates as of Jul 1 ea

”

le Population Profile (NVPP), 1977‐2003; see

NHTSA, “Vehicle Survival and Travel Mileage Sche Regulatory Analysis and Evaluation, NCSA,

January 2006, pp. 9‐11, Docket No. 22223‐2218. P annual census of passenger cars and light trucks

registered for on‐road operation in the United St ch year. NVPP registration data from vehicle

model years 1977 to 2003 were used to develop the survival rates reported in Table VIII‐6. Survival rates were

averaged for the five most recent model years for vehicles up to 20 years old, and regression models were fitted to

these data to develop smooth relationships between age an

age.

16 NHTSA, “Vehicle Survival and Travel Mileage Schedules,

2006, pp. 15‐17 (Docket NHTSA‐2009‐0062‐0012.1). The original

cars and light trucks by age used in this analysis is the 2001

”	

d the proportion of cars or light trucks surviving to that

Office of Regulatory Analysis and Evaluation, January

source of information on annual use of passenger

National Household Travel Survey (NHTS), jointly

sponsored by the Federal Highway Administration, Bureau of Transportation Statistics, and National Highway

Traffic Safety Administration.
33 

Equation 5: Gallons of Fuel Saved per Vehicle

ቈ 	෍ ൌ ሻߜሺ	݁݀ݒܽݏ	ݏ݊݋݈݈݃ܽ	݁݀ݐ݅݉ܽݐݏ݁

ݔ ∗ ଼ ௜ ௜ ௜ ௜ሻݏሺܲ

ݔ ∗ ሻݏሺܲ െ෠ ߠ

ୀଶ ቉ ௜ߠ

Where is the probability of survival for a vehicle of age i,   is the average number miles traveled

by a vehicle of age i, and   is the estimated on‐road fuel economy of a new (2011) vehicle with TPMS,

calculated by combining the average fuel economy for a passenger car (33.8 MPG) or LTV (24.5 MPG)17

with the correction factor employed regularly by the DOT to account for the discrepancy between

estimated fuel economy as reported by the EPA and the observed on‐road fuel economy (this factor

accounts for typical driver behavior and is equal to 0.8)18.   

Equation 6: 2011 Fuel Economy With TPMS

௜ݔ ௜ሻݏሺܲ

ߠ

           ܩܲܯ	27.04 ൌ 0.8 ∗ 33.8 ൌ௉஼ ߠ

ܩܲܯ	19.6 ൌ 0.8 ∗ 24.5 ൌ௏ ் ௅               ߠ

The adjusted fuel economy   is the estimated economy for a hypothetical new vehicle without TPMS,

and is calculated with the following equation:

Equation 7: Estimated 2011 Fuel Economy Without TPMS

෠ߠ

ߠ ஼௉෠ൌ ߠ ∗ ஼௉ሾ1 െ ሺΔ௉஼ሻሿ ൌ 27.04 ∗ ሺ1 െ 0.0022ሻ ൌ 26.98	ܯܲܩ

ߠ௅෠் ௏ൌ ߠ௅் ∗ ௏ሾ1 െ ሺΔ௅்௏ሻሿ ൌ 19.6 ∗ ሺ1 െ .00433ሻ ൌ 19.52	ܯܲܩ

The equations above adjust the fuel economy of a PC or LTV with TPMS by removing the benefit

estimated to result from TPMS.  Substituting and solving for Equation 5 gives:

ߜ ஼௉ൌ 9.32 gallons/vehicle

δ gallons/vehicle ௅்௏ ൌ 27.89

Light trucks and vans are estimated to see a much larger benefit in fuel savings than passenger cars over

their lifetimes.  This is a result of higher TPMS effectiveness, poorer average fuel economy, and more

miles travelled on average for LTVs.  It is important to note that the methods described preclude any

estimation of variance for these quantities because they incorporate several estimates derived by

secondary sources that do not report respective variance estimates.  It is also important to note that the

effectiveness estimates for TPMS that are applied to model year 2011 vehicles are derived from 2004‐

2007 model year vehicles.  The results shown in Figure 8 support this assumption, although there is no

                                                            

17 Research and Innovative Technology Administration (2012). Average Fuel Economy of US Light Duty Vehicles.

Washington, DC; Bureau of Transportation Statistics. [Website].

www.bts.gov/publications/national_transportation_statistics/html/table_04_23.html   18 Parsons, G. G. (1986). Fuel Economy and Annual Travel for Passenger Cars and Light Trucks: National On‐Road

Survey. (Report No. DOT HS 806 971), Washington, DC, National Highway Traffic Safety Administration
34 

way to directly test its validity.   The dollar value of these estimated lifetime fuel savings depends on fuel

prices, but it is likely to account for a substantial portion of the consumer’s cost of a new TPMS, which

was estimated in 2007 dollars to range from $100.00 to $202.30 for a passenger car and from $135.62

to $234.55 for an LTV.

19     

Estimating Total Fleet Fuel Savings During 2011:  A second estimate of interest is the total amount of

money saved during the year of survey data collection (2011) due to reduced fuel consumption across

the fleet.  In order to estimate this quantity, some additional information is needed.  The first is the

percentage of vehicles eight years of age or less that were equipped with TPMS by model year.  The

survey data itself gives this estimate for the model years of interest, 2004‐2011.  A second necessary

estimate is the number of vehicles on the road in 2011 of model years 2003‐2011.  These numbers can

be derived from sales data provided by Ward’s and the survival probabilities listed in Table 14 above.  

Finally, the average fuel economy by model year is required. 20  This number needs to be corrected

because it is an average that includes vehicles with and without TPMS for the model years 2004‐2007.  

The true quantity of interest is the average fuel economy of a vehicle with TPMS, which can be

calculated using the rate of TPMS in a given model year and the estimate of TPMS effectiveness.  In the

previous calculations no correction was necessary because only the fuel economy of 2011 vehicles was

considered, and all vehicles in 2011 were equipped with TPMS.  The correction is made as described in

Equation 7 below.

Equation 7: Average MPG Correction

ܯܲܩ തതതതതതത ൌ ሺݎ∗ߠሻ ൅ ሾሺ1െݎሻ ∗ ߠ෠ሿ

Where is the average EPA corrected fuel economy across vehicles with and without TPMS,   is the

percent of vehicles

෠ߠ

equipped with TPMS,   is the average fuel economy of vehicles with TPMS, and   is

the average fuel economy of a vehicle without TPMS.  We wish to solve for ߠ ;however,

ܩܲܯ

with two

unknowns ( is also unknown) an additional piece of information is necessary.  We can use the

estimated TPMS effectiveness derived earlier to describe the relationship between and   

and thereby arrive at a solvable system of equations.

ଵܩܲܯ ଴

Equation 8: Fuel Economy With and Without TPMS   

ݎ തതതതതതത ܩܲܯ

෠ߠ ߠ

ߠൌߠ ∗ ෠ሺ1 ൅ Δሻ

                                                            

19 These estimates do not reflect any maintenance or repair costs, only the cost of a new system.  Ludtke and

Associates (undated) Cost, Weight Analysis of Tire Pressure Monitoring Systems. (Docket No. NHTSA‐2011‐0066‐

0003). Washington, MI: Ludtke and Associates.  
35 

Table 15: TPMS Share, Sales, and Fuel Economy by Model Year

Passenger Cars

Vehicle

Age (Years)

Model

Year

% With

(   TPMS ࡯ࡼ࢘)

New Vehicle

(   Sales ࡯ࡼࡺ)

Average MPG

ത ࡯ࡼ

)( തതതതതത ࡳࡼࡹ

Average MPG

(   TPMS ࡯ࡼࣂ)

With

1 2011 100 Unk 27.04 27.04

2 2010 100 5,635,433 27.12 27.12

3 2009 100 5,400,890 26.32 26.32

4 2008 100 6,769,107 25.20 25.20

5 2007 66.15 7,562,334 24.96 24.98

6 2006 38.29 7,761,592 24.08 24.11

7 2005 23.79 7,659,983 24.24 24.28

8 2004 29.48 7,482,555 23.60 23.64

Light Trucks and Vans (LTVs)

Vehicle

Age (Years)

Model

Year

% With

(   TPMS ࢂࢀࡸ࢘)

New Vehicle

(   Sales ࢂࢀࡸࡺ)

Average MPG

ത ࢂࢀࡸ

)( തതതതതത ࡳࡼࡹ

Average MPG

(   TPMS ࢂࢀࡸࣂ)

With

1 2011 100 Unk 19.60 19.60

2 2010 100 6,136,787 20.16 20.16

3 2009 100 5,200,478 19.84 19.84

4 2008 100 6,724,058 18.88 18.88

5 2007 77.19 8,897,981 18.48 18.50

6 2006 44.32 9,287,389 18.00 18.04

7 2005 42.61 9,784,346 17.68 17.72

8 2004 27.27 9,816,018 17.20 17.25

Table 15 below shows these additional required estimates for PCs and LTVs separately.

Although the new vehicle sales volume for 2011 was unknown at the time of publication, because there

is assumed zero benefit from TPMS for the first year of operation, this is not crucial information for

estimation.  Equation 7 below calculates the amount of money saved by TPMS in 2011 by estimating the

number of gallons of fuel saved that year by vehicles of model years 2004‐2010 and then multiplying

that estimate by the average price of a gallon of fuel in 2011 ($3.576).20

Equation 9: Dollars Saved in 2011

ቈ ൌ 2011	݅݊	݁݀ݒܽݏ	ݏݎ݈݈ܽ݋݀	݁݀ݐ݅݉ܽݐݏ݁

௜ݔ ∗ ௜ሻݏሺܲ௜ ∗ ݎ ∗ ௜ܰ

෠ߠ

௜

–

௜ݔ ∗ ௜ሻݏሺܲ௜ ∗ ݎ ∗ ௜ܰ

௜ߠ

቉ ∗ $3.576

Where   is the number of vehicles sold of model year i,   is the survival probability to 2011 of a vehicle

of model

௜

year i,   is the average EPA adjusted fuel economy

௜

of a vehicle of model year i, and   is the

ݎ ܰ

෠ߠ ௜ߠ

௜

                                                            

20 US Energy Information Administration, Annual Retail Gasoline and Diesel Prices. Website:

http://www.eia.gov/dnav/pet/pet_pri_gnd_dcus_nus_a.htm.
estimated fuel economy of a vehicle of model year i without TPMS.  Solving this equation for passenger

cars and LTVs separately th

TPMS

ݏݎ݈݈ܽ݋݀

is

݁݀ݒܽݏ	

estimated

	݅݊	2011

to hav

ൌ

e saved

142

en summing

,915,684

gives:

ݏ݊݋݈݈݃ܽ	.6

over 511 million dollars

∗ $3.576 ൌ $511,066,488.

in 2011, and ceteris paribus this estimate

should increase in future years as more vehicles without TPMS are retired and replaced by vehicles with

TPMS.  Had every vehicle in model years 2004‐2011 been equipped with TPMS, the total dollars saved in

2011 would have

This

ݏݎ݈݈ܽ݋݀ሺ

estimate

݁݀ݒܽݏ	

should

	݅݊

been:

	2011

more

|100%

accurat

ܵܯܶܲ	

ely ref

ሻ ൌ

lect

218

the

,378

futur

,259

e expected

ݏ݊݋݈݈݃ܽ	.9

savings

∗ $3.576

resulti

ൌ

n

$

g

780,920

TPMS

,657

from   after

.

the

technology has saturated the fleet, assuming an effective TPMS lifespan of eight years and not

considering any possible additional maintenance expense during this time.

36 

6: Discussion

6.1: Summary of Results

This analysis estimates that TPMS is 55.6‐percent effective at preventing severe underinflation as

defined in FMVSS No. 138.  As a result, vehicles with TPMS should be less likely to be involved in crashes

related to tire failure, long stopping distances, or loss of traction.  Furthermore, these vehicles should

get better gas mileage and should have tires that last longer and provide better response and handling.   

TPMS is also estimated to be 30.7‐percent effective at preventing severe overinflation (at least 25%

above manufacturer’s recommended cold tire pressure).   This may provide further benefits in terms of

safety, efficiency, and component wear.

It is difficult to estimate the number of crashes, injuries, or fatalities that are prevented by TPMS.  The

TPMS‐SS provided the necessary data to create an estimate of the reduction in underinflation due to

TPMS, but current data sources are not sufficient to create an estimate of the number of crashes caused

by underinflation.  Future analyses using real world crash data are necessary to evaluate the effect of

TPMS on crash involvement.

The increase in fuel economy for vehicles with TPMS estimated in this report is an additional benefit of

the technology.  Current benefits were estimated at over five hundred million dollars in fleet‐wide fuel

savings during 2011, and these savings are expected to increase as TPMS saturates the fleet.  These

savings may also increase if direct systems are replaced with advanced indirect systems that require less

maintenance and will not suffer attrition due to battery life span.  Conversely, these savings may

decrease slightly as average vehicle fuel economy increases.
37

6.2: Possible Sources of Bias

Model Year: Two factors may have introduced a large bias to the estimates listed above. The first is a 

model year effect. As shown in Figure 9 below, TPMS is more likely to be found in later model year 

vehicles. 

Fig. 9: Percent of Surveyed Vehicles With TPMS by Model Year

If newer vehicles are more likely to have properly inflated tires for reasons unrelated to TPMS, then this 

interaction would result in an overestimate of TPMS effectiveness. Fortunately, Figure 3 shows that this 

is unlikely. Notice that the rate of underinflation in vehicles without TPMS is relatively constant across 

the included model years. Although there does appear to be a linear model year effect on 

underinflation rate in the vehicles with TPMS, as discussed earlier this could be due to system attrition 

or improvements to TPMS technology. If the underinflation rate of vehicles without TPMS is truly 

constant over time, it would be valid to include the data from model years 2008-2011. However, since 

this can’t be confirmed (there are no vehicles in that model year range without TPMS), these years were 

excluded resulting in a more conservative estimate of TPMS effectiveness.

Vehicle Model: The second factor that may be introducing bias is a vehicle model effect. It is possible 

that the vehicle models equipped with TPMS during one of the specific analyzed model years are 

different from those still without TPMS in the same model year (sportier, more expensive, etc.) and that 

these differences may cause them to be better maintained. In order to address this possible bias a 

supplemental analysis was conducted. Although this supplemental analysis is separate and distinct from 

the primary analysis and its results, it will be described in some detail in the following section because it 

lends insight into the primary analysis and its validity.

0

10

20

30

40

50

60

70

80

2004 2005 2006 2007

Model Year
38

To remove as much of the possible bias introduced by vehicle model the vehicles included in the analysis 

were restricted to models that met one of the two following criteria:

1) Offered TPMS as an option at some point (MY 2004-2010). Only years with vehicle model 

observations both with and without TPMS were included.

2) Transitioned from no available TPMS to standard TPMS in consecutive model years. Only the 

year before and the year after TPMS introduction were included.

The purpose of applying these restrictions is to create a subsample of the survey data that is as free as 

possible from bias caused by differences between the vehicle models equipped with TPMS and the 

vehicle models without TPMS. These restrictions will also reduce bias caused by the linear trend 

observed in TPMS effectiveness across model years. The resulting estimates will be less precise because 

of a reduced sample size, but they will be less susceptible to both vehicle model and model year sources 

of bias. The estimates that result from this analysis are risk ratios as in the principal analysis. The 

results are given in Table 14 below.

Table 14: TPMS Effectiveness – Within Vehicle Model Analysis (MY 2004-2007)

Any 

Underinflation

> 10% 

Underinflation

> 25% 

Underinflation

Risk ratio .951 .581* .39*

Percent Effectiveness

(95% CI)

4.9%

(-4%, 15%)

42.9%*

(25%, 75%)

61%*

(4%, 154%)

*=statistically significant at p<.05

These results are nearly identical to the results of the principal analysis. The confidence intervals are 

larger because the sample sizes are smaller. The similarity of the within-model results gives strong 

evidence that there is no significant vehicle model bias in the principal analysis.

6.3: Limitations

As with any survey, it is important to consider the differences between the population of vehicles that 

had an opportunity to be included in the study and the population to which one wishes to extrapolate. 

In this case, only vehicles that visited large gas stations between the hours of 8 a.m. and 6 p.m. were 

exposed to the sampling process. Also, only vehicles of model year 2004-2011 were eligible, and 

therefore the resulting estimates have no contribution from vehicles more than eight years old. 

The methods of analysis were designed to minimize the effects of likely sources of bias. However it is 

important to note that the effectiveness estimates are created from a very limited range of model year 

vehicles (2004-2007). Also, effectiveness was averaged across these years despite the appearance of a 

linear trend in TPMS effectiveness in preventing underinflation over time. The effect of this averaging is 
that the resulting estimates are likely to be conservative for newer or more recent vehicles, but could be

overstated for earlier or older vehicles.

6.4: Future Research

This analysis raised some questions that may benefit from future research.  The first is the unknown rate

of TPMS attrition.  The observed decline in TPMS effectiveness as vehicle age increases may be better

understood by determining the rate of functioning to non‐functioning TPMS as vehicle age increases.  

This issue could be addressed by a small follow‐up survey, limited to identifying the proportion of

vehicles of various model years with functioning TPMS.

The second question is the effect of overinflation on safety.  It is reasonable to assume that overinflated

tires are less durable from a tread wear standpoint and possibly less safe than tires inflated at the

recommended level.  Although the rate of severe overinflation in the surveyed population was over 10

percent, there is little published research on identifying and quantifying the risks posed by different

levels of overinflation.   

The final question is the association between underinflation and crash incidence.  With an estimate of

the increased probability of a vehicle with severely underinflated tires being involved in a crash, one

could estimate the number of crashes prevented by TPMS. This analysis of crash data is currently

planned to be the next major step in NHTSA’s evaluation of TPMS.

39 
7: Appendix

7.1: Weighting

The TPMS‐SS has a complex sampling structure, with several levels of stratification.  The unit of

observation is the vehicle, and to reach an individual vehicle this study used four separate levels of

selection.  1) The primary sampling units (PSU’s) are geographic regions within the United States that

can represent a city, county, or other region.  2) Within each PSU, several ZIP codes were selected.  3)

Within each ZIP code, two eligible gas stations were selected.  4) Within each gas station, several

vehicles were selected for observation.  

PSU Sampling:

The first stage of weighting at the PSU level has been computed previously and is available in the NASS‐ 

CDS data files.  These weights are adjusted inverse probabilities of selection and can be combined with

the weights from each sampling stage to create the final observation weights.

ZIP Code Sampling:

The ZIP codes are treated as a simple random sample from the frame of ZIP codes within each PSU.  This

means that although the ZIP codes have different population sizes, each ZIP code within a given PSU has

an equal probability of selection.  Let 1≤ i ≤ 24 represent the 24 PSUs included in the study. Let Mi

denote the total number of ZIP codes in PSU i, and let 1 ≤ j ≤ Mi be the j

th ZIP code of the ith PSU, where

mi denotes the total number of selected ZIP codes in the i

th PSU.

Using this notation we can represent the

using the following formula:

probability that ZIP j in PSU i is included in the TPMS‐SS sample

A1 

௜ߜ ൌ௜௝ ܲ

݉௜

௜ܯ

where   denotes the selection probability for the i

th PSU (inverse of the PSU weight from the NASS

database).  Notice that at this stage each sampled ZIP code within a single PSU will have a common

probability of selection.

Gas Station Sampling:

Within each selected ZIP code, two gas stations that met the eligibility requirements were selected for

data collection.  The selection of gas stations within the ZIP codes was done by data collection staff as a

convenience sample.  Because of the method of selection, the gas stations were considered to be

selected with conditional certainty, and each selected station was assigned a weight of 1, or unity.  The

theory here is that given a certain ZIP code, there will be two gas stations that are immutably the most

convenient for the research staff.  These two stations are the most convenient regardless of whether or

not the ZIP code is selected, and if the ZIP code is selected, it is a certainty in terms of probability that

௜ߜ
these two and only these two will be selected by the research staff.  This method of selection was

necessary due to the prohibitive expense of developing an adequate sampling frame, as well as the

additional cost of sending data collection teams to non‐optimal locations.

The omission of a count of eligible gas stations within each sampled ZIP code means that the resulting

final weights can only be used to estimate rates within the sampled population and not the overall size

of the population of interest.  For example, direct application of the weights will not allow a statement

such as ‘There are x vehicles in the United States with at least one severely underinflated tire.’  Rather, it

allows a statement like ‘Within the sampled population, p percent of the vehicles had at least one

severely underinflated tire.’  This estimate may then be combined with existing estimates of the number

of vehicles in the United States in order to arrive at the total number of vehicles with underinflated tires

in the country.         

Vehicle Sampling: The third stage of sampling (vehicle) was conducted on site by the data collection

researchers.  It was not likely that the researchers would be able to approach every vehicle in the

population of interest.  This level of selection was largely based on convenience of the data collection

researchers, although it was considered pseudo‐random and unlikely to bias any estimates.  A census of

vehicles entering the station was taken during five separate evenly spaced fifteen minute intervals

throughou

ܰ෡

t the site observation period.

Let ௜௝௞ be the estimated total number of vehicles that enters the kth site

௜௝௞

of the j

th ZIP code of the ith PSU

during the eight‐hour observation period.  Let nijk

e selection probability can then be given by the follow

௜ߜ ൌ

௜

௜ܯ

௜௝௞

ܰ෡௜௝௞

௞௝௜ܰ ൑ ݈ ൑ 1	ܽ݊݀	௜ܯ ൑ ݇ ൑ 1	ݎ݋݂		

t oijkq be the total number of vehicles observed during

the j

th zip code of the ith PSU.  Then ܰ෡௜௝௞ is estimated a

ܰ෡௜௝௞ ൌ ሺ ௜௝௞௤

6.75ൗ ሻ෍݋

ହ

݉ ݊ ෡

1.25

௤ୀଵ

during the census periods is multiplied by (6.75/1.25)

be the subset of   that is successfully sampled by

the research staff.   The vehicl ing formula:

A2 

ܰ෡

ܲ௜௝௞௟

ܰ෡௜௝௞ is estimated as follows: le the qth vehicle

census period at the k

th site of s:

The sum of observed vehicles to reflect the

respective amount of time that was spent taking vehicle counts (1.25 hrs) and collecting data (6.75 hrs)

during the 8 hour site observation period.     

Correction Factor:

As described previously, the ZIP codes within a given PSU were sampled with equal probability even

though the ZIP codes have different population sizes.  To account for this difference, a correction factor

was derived from 2010 Census data, which gives population by ZIP code.  Let popij be the reported

population of the j

th ZIP code within the ith PSU.  Then the ZIP code size correction factor (θij) is described

as follows:
A3 

ሺ/௜௝݌݋݌ ൌ௜௝ ߠ

௝௜݌݋݌ ∑

௠೔

௝ୀଵ

݉௜

ሻ

Notice that ௠೔ ∑ ߠ . ௝௜ ൌ 1 ௞ୀଵ

Final Observation Weights:

The final observation weights were applied to the vehicle data at the gas station level (every vehicle

observed at a given station had the same weight) and is given by the followi

௜ ௝௜ ௟௞௝௜ݓ

௜ ௜௝௞

ᇱ ൌ ߠ /ሺߜ∗ 

݉௜

ܯ

݊௜௝௞

ܰ ሻ		

bability of selection as well as the ZIP

ng equation:

This represents the inverse of the pro code size correction factor.
A4 

7.2: Survey Forms
DAILY SITE FORM 

REFUELING 

FormApproveo o.w.a. No. 2127~ 

ap1raaon Date: OISIXIf13 

4. Station Cbaracmistics: 0 Cash OliJy 0 ay-«t ~ 0 Casmer Wil>dow 0 Store 0 Car Wash 0 Altto Repair (All tbatapplyj 

A5 
A6 
A7 
A8 
A9 
 

A10 
A11 

SUPPLEMENTAL FORM (Conrlnued) 

14. Have you or someone else checked the vehicle 21. Has your TPMS malfunction lamp ever illuminated , because the warning (combined) lamp was not except duri ng engine on/off cycles? If yes, now working correctly? If yes, what was found to be the many times? (Check one) reason? (Check all that apply) 

1) 0 No, did not check il 1) 0 No 

2) 0 Yes, needed re-set 2) 0 (Approximate 3) 0 Yes. _ _ number of times) Yes, sensors or other parlin the lire not working 

0 3) illuminated s needed to be changed 0 Yes, light is continuously or 4) Yes, batterie

5) 0 Yes comes on regularly , tight bulb needed to be replaced 

6) 0 Yes, general problem with TPMS system 4) 0 Yes. don't know how many times. 

7) 0 Yes, don't know 5) 0 Don't know if illuminated 

8) 0 Yes, other (specify)-------

15. Do you know how to reset (Calibrate) your TPMS? IF RESPONSE TO QUESTION #21 IS "YES" , CONTINUE; 

H yes, how do you do it? (Check one) IF NOT, GO TO THE END 

1) ONo 

22. When was the last time the malfunction lamp 2) 0 Yes. use button in vehicle 

0 illuminated on this vehicle? (Check one) 3) Yes. follow menu on dlspray 1) 0 Within the past month 

4) 0 Yes. only dealer/service station can do it 2) Ot-2 months ago 

5) 0 Yes. other (specify) ------ 3) 0 3-4 months ago 

16. When 4) 0 More t han 4 months ago should your TPMS be reset? (Check all that apply) 

1) 0 Never 5) 0 COnlin,uoustylrepetitively 

2) 0 When the tire pressure Is Checked 6) 0 Don't know 

3) 0 When the tire pressure Is Changed 

4) 0 When a lire is changed 23. What actions did you take the last time the TPMS 

5) 0 When the tires are rotated malfunction lamp illuminated? (Check all that apply) 

6) 0 Don't know 1) 0 Did noth otten illuminates 7) 0 ing-it other (specify). _ _____ _ 2) 0 Did nothing-other reasons 

3) 17. ) 0 Reset the TPMS How easy or difficult is it to reset your TPMS? (O.eckone

4) 0 0 Took vehicle to the dealer or a service facility 1) Very easy 5) 0 0 SeH or others worked on vehicle 2) Somewhat easy 6) 0 other (specify). ______ _ 

3) 0 Somewhat difficult 

4) 0 Very difficult 24. Have you or someone else checked the vehicle 

5) 0 because the malfunction lamp was not working Don't know correctly? If yes, w hat was found to be the reason? 

18. To w hat extent do you rely on your TPMS to tell you (Check all that apply) 

when your tires need air? (Check one) 1) 0 No, did not check it 

1) 0 Rely fully on the TPM S 2) 0 Yes. needed re-set 

2) 0 Rely on TPMS, but also use other methods 3) 0 Yes. sensors or other part in the tire not 

working 3) 0 Don't rely on TPMS, only use other methods 4) 0 Yes, batteries needed to be Changed 

19. Does your TPMS have a malfunction lamp? {Ctleckone) 5) 0 Yes, light bulb needed to be replaced 

0 No 0 Yes 0 Don't Know 6) 0 Yes, general problem with TPMS system 

7) 0 Yes, don't know 

IF RESPONSE TO QUESTION 119 IS • YES". CONTINUE; 8) 0 Yes. other (specify) _____ _ IF NOT, GO TO THE END 

{Questions on TPMS M alfunction Warning Lamp] 

20. Do you know where your TPMS malfunction lamp is THE END located? If yes, where? (Check one) 

1) 0 No THANK YOU FOR YOUR PARTICIPATION 

2) 0 Yes, on instrument panel 

3) 0 Yes, on rearview mirror 

4) 0 Yes. roof console 

5) 0 Yes, other (specify)------

IF RESPONSE TO QUESTION 120 IS • YES" , 

CONTINUE; IF NOT, GO TO THE END 
DOT HS 811 681 

November 2012

8915-110212-v4